What Is Problem Solving? How Software Engineers Approach Complex Challenges

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From debugging an existing system to designing an entirely new software application, a day in the life of a software engineer is filled with various challenges and complexities. The one skill that glues these disparate tasks together and makes them manageable? Problem solving . 

Throughout this blog post, we’ll explore why problem-solving skills are so critical for software engineers, delve into the techniques they use to address complex challenges, and discuss how hiring managers can identify these skills during the hiring process. 

What Is Problem Solving?

But what exactly is problem solving in the context of software engineering? How does it work, and why is it so important?

Problem solving, in the simplest terms, is the process of identifying a problem, analyzing it, and finding the most effective solution to overcome it. For software engineers, this process is deeply embedded in their daily workflow. It could be something as simple as figuring out why a piece of code isn’t working as expected, or something as complex as designing the architecture for a new software system. 

In a world where technology is evolving at a blistering pace, the complexity and volume of problems that software engineers face are also growing. As such, the ability to tackle these issues head-on and find innovative solutions is not only a handy skill — it’s a necessity. 

The Importance of Problem-Solving Skills for Software Engineers

Problem-solving isn’t just another ability that software engineers pull out of their toolkits when they encounter a bug or a system failure. It’s a constant, ongoing process that’s intrinsic to every aspect of their work. Let’s break down why this skill is so critical.

Driving Development Forward

Without problem solving, software development would hit a standstill. Every new feature, every optimization, and every bug fix is a problem that needs solving. Whether it’s a performance issue that needs diagnosing or a user interface that needs improving, the capacity to tackle and solve these problems is what keeps the wheels of development turning.

It’s estimated that 60% of software development lifecycle costs are related to maintenance tasks, including debugging and problem solving. This highlights how pivotal this skill is to the everyday functioning and advancement of software systems.

Innovation and Optimization

The importance of problem solving isn’t confined to reactive scenarios; it also plays a major role in proactive, innovative initiatives . Software engineers often need to think outside the box to come up with creative solutions, whether it’s optimizing an algorithm to run faster or designing a new feature to meet customer needs. These are all forms of problem solving.

Consider the development of the modern smartphone. It wasn’t born out of a pre-existing issue but was a solution to a problem people didn’t realize they had — a device that combined communication, entertainment, and productivity into one handheld tool.

Increasing Efficiency and Productivity

Good problem-solving skills can save a lot of time and resources. Effective problem-solvers are adept at dissecting an issue to understand its root cause, thus reducing the time spent on trial and error. This efficiency means projects move faster, releases happen sooner, and businesses stay ahead of their competition.

Improving Software Quality

Problem solving also plays a significant role in enhancing the quality of the end product. By tackling the root causes of bugs and system failures, software engineers can deliver reliable, high-performing software. This is critical because, according to the Consortium for Information and Software Quality, poor quality software in the U.S. in 2022 cost at least $2.41 trillion in operational issues, wasted developer time, and other related problems.

Problem-Solving Techniques in Software Engineering

So how do software engineers go about tackling these complex challenges? Let’s explore some of the key problem-solving techniques, theories, and processes they commonly use.

Decomposition

Breaking down a problem into smaller, manageable parts is one of the first steps in the problem-solving process. It’s like dealing with a complicated puzzle. You don’t try to solve it all at once. Instead, you separate the pieces, group them based on similarities, and then start working on the smaller sets. This method allows software engineers to handle complex issues without being overwhelmed and makes it easier to identify where things might be going wrong.

Abstraction

In the realm of software engineering, abstraction means focusing on the necessary information only and ignoring irrelevant details. It is a way of simplifying complex systems to make them easier to understand and manage. For instance, a software engineer might ignore the details of how a database works to focus on the information it holds and how to retrieve or modify that information.

Algorithmic Thinking

At its core, software engineering is about creating algorithms — step-by-step procedures to solve a problem or accomplish a goal. Algorithmic thinking involves conceiving and expressing these procedures clearly and accurately and viewing every problem through an algorithmic lens. A well-designed algorithm not only solves the problem at hand but also does so efficiently, saving computational resources.

Parallel Thinking

Parallel thinking is a structured process where team members think in the same direction at the same time, allowing for more organized discussion and collaboration. It’s an approach popularized by Edward de Bono with the “ Six Thinking Hats ” technique, where each “hat” represents a different style of thinking.

In the context of software engineering, parallel thinking can be highly effective for problem solving. For instance, when dealing with a complex issue, the team can use the “White Hat” to focus solely on the data and facts about the problem, then the “Black Hat” to consider potential problems with a proposed solution, and so on. This structured approach can lead to more comprehensive analysis and more effective solutions, and it ensures that everyone’s perspectives are considered.

This is the process of identifying and fixing errors in code . Debugging involves carefully reviewing the code, reproducing and analyzing the error, and then making necessary modifications to rectify the problem. It’s a key part of maintaining and improving software quality.

Testing and Validation

Testing is an essential part of problem solving in software engineering. Engineers use a variety of tests to verify that their code works as expected and to uncover any potential issues. These range from unit tests that check individual components of the code to integration tests that ensure the pieces work well together. Validation, on the other hand, ensures that the solution not only works but also fulfills the intended requirements and objectives.

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Evaluating Problem-Solving Skills

We’ve examined the importance of problem-solving in the work of a software engineer and explored various techniques software engineers employ to approach complex challenges. Now, let’s delve into how hiring teams can identify and evaluate problem-solving skills during the hiring process.

Recognizing Problem-Solving Skills in Candidates

How can you tell if a candidate is a good problem solver? Look for these indicators:

  • Previous Experience: A history of dealing with complex, challenging projects is often a good sign. Ask the candidate to discuss a difficult problem they faced in a previous role and how they solved it.
  • Problem-Solving Questions: During interviews, pose hypothetical scenarios or present real problems your company has faced. Ask candidates to explain how they would tackle these issues. You’re not just looking for a correct solution but the thought process that led them there.
  • Technical Tests: Coding challenges and other technical tests can provide insight into a candidate’s problem-solving abilities. Consider leveraging a platform for assessing these skills in a realistic, job-related context.

Assessing Problem-Solving Skills

Once you’ve identified potential problem solvers, here are a few ways you can assess their skills:

  • Solution Effectiveness: Did the candidate solve the problem? How efficient and effective is their solution?
  • Approach and Process: Go beyond whether or not they solved the problem and examine how they arrived at their solution. Did they break the problem down into manageable parts? Did they consider different perspectives and possibilities?
  • Communication: A good problem solver can explain their thought process clearly. Can the candidate effectively communicate how they arrived at their solution and why they chose it?
  • Adaptability: Problem-solving often involves a degree of trial and error. How does the candidate handle roadblocks? Do they adapt their approach based on new information or feedback?

Hiring managers play a crucial role in identifying and fostering problem-solving skills within their teams. By focusing on these abilities during the hiring process, companies can build teams that are more capable, innovative, and resilient.

Key Takeaways

As you can see, problem solving plays a pivotal role in software engineering. Far from being an occasional requirement, it is the lifeblood that drives development forward, catalyzes innovation, and delivers of quality software. 

By leveraging problem-solving techniques, software engineers employ a powerful suite of strategies to overcome complex challenges. But mastering these techniques isn’t simple feat. It requires a learning mindset, regular practice, collaboration, reflective thinking, resilience, and a commitment to staying updated with industry trends. 

For hiring managers and team leads, recognizing these skills and fostering a culture that values and nurtures problem solving is key. It’s this emphasis on problem solving that can differentiate an average team from a high-performing one and an ordinary product from an industry-leading one.

At the end of the day, software engineering is fundamentally about solving problems — problems that matter to businesses, to users, and to the wider society. And it’s the proficient problem solvers who stand at the forefront of this dynamic field, turning challenges into opportunities, and ideas into reality.

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Lesson 27 of 33 By Hemant Deshpande

An Ultimate Guide That Helps You to Develop and Improve Problem Solving in Programming

Table of Contents

Coding and Programming skills hold a significant and critical role in implementing and developing various technologies and software. They add more value to the future and development. These programming and coding skills are essential for every person to improve problem solving skills. So, we brought you this article to help you learn and know the importance of these skills in the future. 

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Topics covered in this problem solving in programming article are:

  • What is Problem Solving in Programming? 
  • Problem Solving skills in Programming
  • How does it impact your career ?
  • Steps involved in Problem Solving
  • Steps to improve Problem Solving in programming

What is Problem Solving in Programming?

Computers are used to solve various problems in day-to-day life. Problem Solving is an essential skill that helps to solve problems in programming. There are specific steps to be carried out to solve problems in computer programming, and the success depends on how correctly and precisely we define a problem. This involves designing, identifying and implementing problems using certain steps to develop a computer.

When we know what exactly problem solving in programming is, let us learn how it impacts your career growth.

How Does It Impact Your Career?

Many companies look for candidates with excellent problem solving skills. These skills help people manage the work and make candidates put more effort into the work, which results in finding solutions for complex problems in unexpected situations. These skills also help to identify quick solutions when they arise and are identified. 

People with great problem solving skills also possess more thinking and analytical skills, which makes them much more successful and confident in their career and able to work in any kind of environment. 

The above section gives you an idea of how problem solving in programming impacts your career and growth. Now, let's understand what problem solving skills mean.

Problem Solving Skills in Programming

Solving a question that is related to computers is more complicated than finding the solutions for other questions. It requires excellent knowledge and much thinking power. Problem solving in programming skills is much needed for a person and holds a major advantage. For every question, there are specific steps to be followed to get a perfect solution. By using those steps, it is possible to find a solution quickly.

The above section is covered with an explanation of problem solving in programming skills. Now let's learn some steps involved in problem solving.

Steps Involved in Problem Solving

Before being ready to solve a problem, there are some steps and procedures to be followed to find the solution. Let's have a look at them in this problem solving in programming article.

Basically, they are divided into four categories:

  • Analysing the problem
  • Developing the algorithm
  • Testing and debugging

Analysing the Problem

Every problem has a perfect solution; before we are ready to solve a problem, we must look over the question and understand it. When we know the question, it is easy to find the solution for it. If we are not ready with what we have to solve, then we end up with the question and cannot find the answer as expected. By analysing it, we can figure out the outputs and inputs to be carried out. Thus, when we analyse and are ready with the list, it is easy and helps us find the solution easily. 

Developing the Algorithm

It is required to decide a solution before writing a program. The procedure of representing the solution  in a natural language called an algorithm. We must design, develop and decide the final approach after a number of trials and errors, before actually writing the final code on an algorithm before we write the code. It captures and refines all the aspects of the desired solution.

Once we finalise the algorithm, we must convert the decided algorithm into a code or program using a dedicated programming language that is understandable by the computer to find a desired solution. In this stage, a wide variety of programming languages are used to convert the algorithm into code. 

Testing and Debugging

The designed and developed program undergoes several rigorous tests based on various real-time parameters and the program undergoes various levels of simulations. It must meet the user's requirements, which have to respond with the required time. It should generate all expected outputs to all the possible inputs. The program should also undergo bug fixing and all possible exception handling. If it fails to show the possible results, it should be checked for logical errors.

Industries follow some testing methods like system testing, component testing and acceptance testing while developing complex applications. The errors identified while testing are debugged or rectified and tested again until all errors are removed from the program.

The steps mentioned above are involved in problem solving in programming. Now let's see some more detailed information about the steps to improve problem solving in programming.

Steps to Improve Problem Solving in Programming

Right mindset.

The way to approach problems is the key to improving the skills. To find a solution, a positive mindset helps to solve problems quickly. If you think something is impossible, then it is hard to achieve. When you feel free and focus with a positive attitude, even complex problems will have a perfect solution.

Making Right Decisions

When we need to solve a problem, we must be clear with the solution. The perfect solution helps to get success in a shorter period. Making the right decisions in the right situation helps to find the perfect solution quickly and efficiently. These skills also help to get more command over the subject.

Keeping Ideas on Track

Ideas always help much in improving the skills; they also help to gain more knowledge and more command over things. In problem solving situations, these ideas help much and help to develop more skills. Give opportunities for the mind and keep on noting the ideas.

Learning from Feedbacks

A crucial part of learning is from the feedback. Mistakes help you to gain more knowledge and have much growth. When you have a solution for a problem, go for the feedback from the experienced or the professionals. It helps you get success within a shorter period and enables you to find other solutions easily.

Asking Questions

Questions are an incredible part of life. While searching for solutions, there are a lot of questions that arise in our minds. Once you know the question correctly, then you are able to find answers quickly. In coding or programming, we must have a clear idea about the problem. Then, you can find the perfect solution for it. Raising questions can help to understand the problem.

These are a few reasons and tips to improve problem solving in programming skills. Now let's see some major benefits in this article.

  • Problem solving in programming skills helps to gain more knowledge over coding and programming, which is a major benefit.
  • These problem solving skills also help to develop more skills in a person and build a promising career.
  • These skills also help to find the solutions for critical and complex problems in a perfect way.
  • Learning and developing problem solving in programming helps in building a good foundation.
  • Most of the companies are looking for people with good problem solving skills, and these play an important role when it comes to job opportunities 
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Problem solving in programming skills is important in this modern world; these skills build a great career and hold a great advantage. This article on problem solving in programming provides you with an idea of how it plays a massive role in the present world. In this problem solving in programming article, the skills and the ways to improve more command on problem solving in programming are mentioned and explained in a proper way.

If you are looking to advance in your career. Simplilearn provides training and certification courses on various programming languages - Python , Java , Javascript , and many more. Check out our Post Graduate Program in Full Stack Web Development course that will help you excel in your career.

If you have any questions for us on the problem solving in programming article. Do let us know in the comments section below; we have our experts answer it right away.

About the Author

Hemant Deshpande

Hemant Deshpande, PMP has more than 17 years of experience working for various global MNC's. He has more than 10 years of experience in managing large transformation programs for Fortune 500 clients across verticals such as Banking, Finance, Insurance, Healthcare, Telecom and others. During his career he has worked across the geographies - North America, Europe, Middle East, and Asia Pacific. Hemant is an internationally Certified Executive Coach (CCA/ICF Approved) working with corporate leaders. He also provides Management Consulting and Training services. He is passionate about writing and regularly blogs and writes content for top websites. His motto in life - Making a positive difference.

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A Guide to Problem-Solving for Software Developers with Examples

If I ask you, out of the blue, what’s the role of a developer, what would you answer? Coding all day? Drinking coffee? Complaining about the management?

To me, a developer is first and foremost a problem solver, simply because solving problem is the most important (and the most difficult) part of our job. After all, even if our code is perfect, clear, performing great, a masterpiece of form and meaning, it’s useless if it doesn’t solve the problem it was meant to solve.

So, let’s dive into problem-solving today. More specifically, we’ll see in this article:

  • How to define a problem, and the difference sometimes made between problem-solving and decision-making.
  • Why some problems should not be solved.
  • The two wide categories of problems you can encounter.
  • Why it’s important to correctly define the problem, and how to do so.
  • How to explore the solution space.
  • Why deferring a problem might be the best decision to make in specific situations.
  • Why reflecting on the whole process afterward can help you in the future.

This article is mostly based on my own experience, even if I apply here some ideas I found in books and papers.

We have our plan. Now, it’s time to dive deep into the difficult, but rewarding, process of problem-solving.

Problem-Solving and Decision-Making

“When I use a word,” Humpty Dumpty said in rather a scornful tone, “it means just what I choose it to mean — neither more nor less.” “The question is,” said Alice, “whether you can make words mean so many different things.” “The question is,” said Humpty Dumpty, “which is to be master — that’s all.” Lewis Caroll Source

Words are ambiguous; they can mean different things for each of us. So let’s first begin to agree on the definition of “problem-solving” here, to be sure we’re on the same page.

Let’s first look at the definition of the word “problem” in a dictionary:

  • According to the American Heritage Dictionary , a problem is “a question to be considered, solved, or answered”.
  • According to the Oxford Learner’s dictionary , a problem is “a thing that is difficult to deal with or to understand”.

In short, in any problem, there is some degree of uncertainty. If you’re certain of the solution, the problem is already solved. Nothing would need to be “considered, solved, or answered”.

Information is useful to reduce this uncertainty. The quantity is often not the most important, but the quality will be decisive. If I tell you that 90% of my readers are extremely intelligent, would it help you to solve a problem in your daily job? I bet it wouldn’t. It’s information nonetheless, but its usefulness for you is close to zero.

This is an extreme example, but it highlights an important point: before collecting any data, define your problem clearly; then, according to the problem, decide what data you need. Yet, many companies out there begin to collect the data and then decide what problem to solve. We’ll come back to that soon in this article.

So, to summarize, a problem is a situation with some degree of uncertainty. Sometimes, this uncertainty needs to be reduced to come up with an appropriate solution, or, at least, a decision to move forward to your specific goal.

Is there a Problem to Solve?

Whenever you (or somebody else) see a problem, you should always ask yourself this simple question first: is it really a problem, and should we solve it now ?

In other words, ask yourself the following questions:

  • Why is this problem important to solve?
  • Would be solving the problem creates some value? What value?
  • What would happen if the problem was not solved?
  • What desired outcome do we expect by solving the problem?

If the problem doesn’t bother anybody and solving it doesn’t create any value, why allocating effort and time to solve it?

It sounds obvious, but it’s an important point nonetheless. More often than not, I see developers heading first in solving problems without asking themselves if they should solve them at the first place.

The most common examples I can think of are useless refactoring. I saw developers refactoring parts of codebases which never change, or is rarely executed at runtime. In the mind of the developer, the code itself is the problem: refactoring is the solution.

I remember a similar case: a developer refactored part of the codebase which was basically never used. We discovered, months later, when we had more and more users using this specific part of the codebase, that the refactoring didn’t really simplify anything. To the contrary; we had to refactor the code again. The first refactoring tried to solve a problem which didn’t exists.

Of course, the developer could argue that the value created is a “cleaner” codebase, but it’s arguable, especially when the code is neither often modified nor used. The value created here is not clear, and it would have been easier if the first refactoring never happened. In this specific situation, I recommend refactoring when you actively change part of the codebase for another reason (implementing a new feature for example).

Whether a problem is worthy to be solved is subjective. It also depends on the problem: if the solution is clear and straightforward, it might be useful to solve it, if the consequences of the solution are also clearly known and the risks are low. Unfortunately, these kinds of problems, in practice, are quite rare.

Types of Problems

I would define here two wide categories of problems: the problems with a (or multiple) clear solution (what the literature call “problem-solving”), and the problems without clear solution (it’s sometimes called “decision-making” instead of “problem-solving”).

In fact, if the problem you’re trying to solve has a clear, accepted answer, it’s very likely it has been solved already. It’s often the case for mechanical, technical problems. For example, let’s say that you need to order a list; you just have to search on the wild Internet how to do so in your programming language of choice, and you’re done! You can ask an “AI” too, or stack overflow, or whatever.

In my experience, most technical problems have one (or multiple) accepted solution. I won’t speak about these kinds of problems at length in this article, since they’re the easiest to solve.

When you’re in front of a problem which has no clear solution (even after doing some research), it’s where things get more complicated. I’d argue that most problems you’ll face, as a software developer, are of this category. Problems which are directly linked to the domain of the company you work with are often specific (because they depend on the domain), and complex.

For example, I’m working for a company providing a learning platform for medical students who want to become doctors, among other services. This context is changing because the real world is changing; medicine is no exception.

Recently, we had to create new data structures for the knowledge we provide; these data structures are directly linked to the domain (medicine) here. But what data structures to create? How can they adapt to the ever-changing environment? How to capture the data in the most meaningful way, with understandable naming for other developers?

Decisions had to be made, and when there are no clear solutions, you need to come up with a couple of hypothesizes. They won’t feel necessary like solutions , but rather decisions to take to move forward toward the desired outcome. It often ends up in compromises, especially if you’re working in a team where the members have different opinions .

Also, architectural decisions have often no clear solutions because they depend, again, on the changing context. How to be sure that an architectural decision is good today and in three months? How can we make the architecture flexible enough to adapt to the blurry future?

As developers, we deal with complex codebases, which are somewhat linked to the even more complex real world. It’s difficult to know beforehand the consequences of our decisions, as well as the benefits, the drawback, and the potential bugs we introduce.

Before jumping into the solution space however, we first need a good detour in the problem space.

Defining the Problem

Correctly stating the problem.

After determining that we indeed have some kind of problem, it’s tempting to try to find a solution directly. Be patient: it’s better to look at the problem more closely first.

If you don’t specify well the problem, you might not solve it entirely. It’s also possible that you end up solving the wrong problem, or the symptoms of a problem, that is, other minor problems created by a root problem. Often, the ideal scenario is to find the root problem, even if you don’t want to tackle it first. In any case, it’s always useful information.

For example, not long ago, our users didn’t find the content they were searching for, using our search functionality on our learning platform.

We could have directly solved the problem by asking the search team to adjust that for us, but this problem was only a symptom. It wasn’t the first time that we had to spend time and energy trying to communicate to the search team what we wanted to fix; the real root problem here was that we didn’t have any ownership of our search results.

The solution: we created a better API communicating with the search team, to be able to adjust ourselves the search results in a more flexible manner.

When looking at a problem, a good first step is to write it down. Don’t do it once; try to find different formulations for the same problem.

Writing is nice (I love it!), but other ways to represent ideas can be really useful too. You can try to draw what you understand from the problem: a drawing, a diagram, or even a picture can help you understand the problem.

From there, you can ask yourself: do you have enough information to take a decision? The answer will be mostly based on the experience of the problem solver, there is no magical formula to be sure that you can and will solve the problem.

You should also try to look at the problem from different angles, to really frame it correctly. The best way to do so is to solve problems as a team.

Solving Problems in a Team

Trying to describe and think about a problem is a great beginning, but it’s even better if you do it as a team. You can exchange experience, opinions, and it’s easier to look at a problem from multiple angles when multiple developers are involved.

First, make sure that everybody in the team is aware of the problem. Defining it altogether is the best. If you have a doubt that somebody is not on the same page, you can re-explain it using different words. It might bring more insights and ideas to the discussion.

Don’t assume that everybody understands the problem equally. Words are powerful, but they are also ambiguous; never hesitate to ask questions (even if they seem stupid at first), and encourage the team to do the same. If your colleagues see that you’re not afraid to ask, it will give them confidence to do the same.

The ambiguity can also build overtime, after the problem was discussed. That’s why it’s really important to document the whole process, for anybody to be able to look at it again and fix the possible creeping misconceptions. Don’t try to describe everything, but try to be specific enough. It’s a delicate balance, and you’ll get better at it with experience.

If you don’t like writing, I’d recommend you to try anyway: this is a powerful skill which will be useful in many areas of your life.

Regarding the team of problem solvers, diversity is important. Diversity of opinion, experience, background, you name it. The more diverse the opinions and ideas are, the more chances you’ll have to solve the problem satisfyingly (more on that later). If the members of the team have enough respect, humility, and know how to listen to their colleagues , you’re in the perfect environment to solve problems.

As developers, we’re dealing with moving systems, because they need to reflect the ever-changing business domain of the company you’re working with. These problems are unique, and even if similar problems might have been solved in the past, they’re never the exactly same. The differences can have an impact on the solution, sometimes insignificant (allowing you to re-apply the solution found previously), sometimes important enough to change the solution entirely.

Exploring the Solution Space

Now that we’ve defined the problem, thought about it with our team, tried to look at it from different angles, it’s time to try to find solutions, or at least to make a decision.

What is a good decision? The one which will bring you closer to your desired outcome. It sounds obvious, but there can be some ego involved in discussions, which will push us to try to be right even if it’s not the best solution in the current context. Our personal incentives can conflict with the company’s best interest; it’s always good to try to stay aware of that.

The solution should also be the simplest possible, while still moving forward to the desired outcome. It should also have an acceptable level of risk when we decide to apply the solution. In my experience, complicated solutions are the ones which come up first: don’t stop there. Take some time trying to find the best solution with your team.

For example, here’s what we do with my actual team:

  • We define the problem altogether.
  • We try to think about different hypothesizes. Not only one, but a couple of them.
  • We write the benefits and drawbacks of each hypothesis (which can lead to more ideas, and possibly more hypothesizes).
  • We commit to a hypothesis, which then needs to be implemented.

What I meant by “hypothesis” here is a solution which might work; but only the implementation of the hypothesis can be considered as a solution. Before the implementation, it’s just an informed guess. Many things can go wrong during an implementation.

This process looks simple, but when you have multiple developers involved, it’s not. Again, if each member of the team have good soft skills and some experience, it can be an enjoyable and rewarding process. But you need a good team for it to work efficiently (that’s why it’s so important to ask the good questions when joining a company). It’s even better if the members of the team are used to swim in uncertainty, and take it as a challenge more than a chore.

The process described above is just an example; in practice it’s often more chaotic. For example, even when a decision is made, your brain might still continue to process the problem passively. If you find some flaws in the hypothesis you’ve committed to, congratulations! You have now a brand-new problem.

I can’t emphasize it enough: try to be as detached as possible from your ideas, opinions, and preferred hypothesizes. The goal is not for you to be right and feel good, but for your company to move in the good direction. It’s hard, but with practice it gets easier.

I also want to underline the importance of finding both benefits and drawbacks for the different hypothesizes you (and your team) came up with.

To find good solutions, we might also need to reduce the uncertainty around their possible consequences. Doing some external research can help, like gathering data around the problem and the possible hypothesizes. In the best case scenario, if you can find enough data, and if you feel confident that you can move forward with a hypothesis, that’s already a great victory.

If you don’t have enough external information to reduce the uncertainty to a level you feel comfortable with, look at your past experience. Try to find problems similar to the one your deal with in the present, and try to think about the solutions applied at the time, to see if they could also be applied in your current case. But be careful with this approach: complex problems are context-sensitive, and the context you were in the past will never be exactly the same as the present and future contexts.

For example, I recently changed the way we display search results in our system, because we had some data indicating that some users had difficulties to find what they really wanted to find. The problem: users have difficulties to find the good information; it’s a recurrent problem which might never be 100% solved. That said, thanks to the data gathered, we found an easy way to improve the situation.

The data was very clear and specific, but it’s not always the case. More often than not, your data won’t really prove anything. It might only show correlations without clear causality. It will be even more true if you begin by gathering data without defining first the problem you try to solve. You can find problems looking at some data, that’s true, but it needs care and deep understanding of what you’re doing; looking at data when you know exactly what you want to solve works better.

Using this kind of process, the hypothesis is often some sort of compromise. That’s fine; committing to a hypothesis is not the end of the process, and there will be other occasions to revisit and refine the solution.

If you don’t feel comfortable with the level of uncertainty of the problem (or the risk involved by applying your hypothesis), you need to dig more. Writing a prototype can be useful for example, if you hesitate between two or more approaches. If your prototype is convincing enough, it can also be useful to gather feedback from your users, even if the ones testing your hypothesis will always be more invested if they test a real-life functionality, instead of a prototype which might use dummy data, or be in a context which is too remote from the “real” context.

In my opinion, prototypes are not always useful for complex problems, because a prototype only test a new feature at time T, but doesn’t allow you to see if the solution stay flexible enough overtime. That’s often a big concern: how will the solution evolve?

But prototyping can still help gather information and reduce the uncertainty of the problem, even if the prototype doesn’t really give you the solution on a silver platter. It’s also great for A/B testing, when you’re in the (likely) case when you have not much information about the real needs of your users. You could ask them of course, but nothing guarantee that they know themselves what these needs are.

If you don’t find any satisfying hypothesis to your problem, you might also challenge the desired outcome. Maybe a similar, simplest hypothesis, with slightly different outcomes, could work better? If it makes things easier, faster, and less complex, it could be the best solution. Don’t hesitate to challenge your stakeholders directly on the desired outcomes.

Deferring the Problem

In some cases, you might be hesitant to try to solve a problem if there is still too much uncertainty around it. In that case, it might be best to defer solving the problem altogether.

Deferring the problem means that you don’t solve it now ; you keep things as they are, until you get more information to reduce the uncertainty enough.

We had a problem in the company I worked with some time ago: we have dosages which can be discovered in articles, but users didn’t really find them, and nobody really knew why. Because of this lack of information, the problem was not tackled right away, but differed. From there, data have been collected overtime, allowing us to understand the scope of the problem better.

Don’t forget that deferring a problem is already taking a decision. It might be the less disruptive decision for the application and its codebase, but it’s s decision nonetheless, and it can have consequences. Seeing a differed problem as a decision will push you to think about the possible consequences of your inaction, and you’ll look at it as a partial “solution”, with some uncertainty and risk associated to it.

In my experience, deferring the problem works well only when you try to actively seek more data to solve it later. It can be some monitoring to see how the problem evolves, or some data taken from users’ actions. Sometimes, simply waiting can also give you important information about the nature of the problem.

What you shouldn’t do is try to forget the problem. It might come back in force to haunt your sleepless nightmares later. Avoiding a problem is not deferring it.

Here’s another example: we began recently to build some CMS tooling for medical editors, for them to write and edit content on our learning platform. We had one GraphQL API endpoint at the beginning, providing data to two different part of the application:

  • Our CMS for medical editors.
  • Our learning platform for medical students.

We knew that using one single GraphQL endpoint for these two types of users could cause some problems.

But we didn’t do anything about it, mostly because we didn’t see any real, concrete problem, at least at first. When a minor symptom, related to this unique endpoint, popped up, we spoke about it, and we still chose not to do anything. We preferred deferring the problem once more, to try to solve the real problem (one API for two different kinds of applications) later.

Finally, when we had enough symptoms and some frustration, we decided to split our graphQL API in two different endpoints. It was the best moment to do so: we had enough information to come up with a good decision, we applied it, and we stayed vigilant, to see how our applied hypothesis would evolve.

Moving fast and breaking things is not always the best solution. In some situations, waiting a bit and see how things evolve can allow you to solve your problems in a more effective way. But, as always, it depends on the problem, its context, and so on.

Reading this article, you might have wondered: how much information is enough to be comfortable enough to apply a solution? Well, again, your experience will be the best judge here. You’ll also need to consider carefully risks, benefits, and drawbacks. It doesn’t mean that you need to chicken out if you don’t have 100% certainty about a problem and some hypothesizes; being a software developer implies to have some courage and accept that mistakes will be made. It’s not an easy task, and there is no general process to follow in any possible case.

In short: use your brain. Even if you’re totally wrong, you’ll have the opportunity to fix the bad decisions you’ve made before the implementation, during the implementation, and even after it. We don’t code in stone.

The Implementation: The Value of Iteration

You’ve gathered with your team, tried to define the problem, found multiple hypothesizes, and agreed to try one of them. Great! Problem solved.

Not so fast! We still need to apply the hypothesis, and hope that it will become a good solution to the problem. Doing so, you’ll gather more information along the way, which might change your perspective on the problem, on your hypothesizes, and can even create some baby problems on its own.

It’s where the agile methodology is useful: since we’ll never have 100% certainty regarding a problem and its possible solution, we’ll learn more about both while implementing the hypothesis. That’s why it’s so valuable to iterate on the implementation: it gives you more information to possibly adjust your code, or even the problem, or even switching hypothesizes altogether. Who knows? A solution which is not implemented is just a guess.

If the hypothesis applied is not the ones you would have personally preferred (compromising, or even giving up on your preferred solution is common in a team), only applying it will tell you if you’re right or wrong; that is, if the hypothesis can become a solution solving the problem, at least in the present context.

If you’re worried about how a specific solution will evolve overtime, it’s more complicated, because an implementation won’t give you the information you seek. Still, implementing a hypothesis can be a great source of learning (the most valuable to me is when I’m wrong, because I learn even more). If you think that your hypothesis can have better outcome at time T, you might also try to implement it and compare it. Again, it’s where prototyping is useful.

When applying the solution, you need to look at the details of the implementation, as well as the big picture, to judge if the solution you’re creating is appropriate (leading to the desired outcome). This is a difficult exercise. In general, a developer should be able to reason on different levels of abstraction, more or less at the same time. Again, if you’re aware of it, your experience will help you here, and you can also push yourself to think of all the possible risks and consequences at different levels.

If you work in a team, try to participate (at least a bit) into the implementation of the solution. It’s not good to create silos in teams (that is, only a couple of members have some information others don’t have).

You can go as far as looking at other projects, and ask yourselves these questions:

  • Did we had similar problems on these other projects? How did we solve them?
  • What was the context of these projects? Is it similar to our current context?
  • What did we learn from these other problems, and their implementation? Is the implementation similar to what we’re doing now?

In any case, I would definitely recommend you to write a development journal. I write mine for years, and it has been valuable in many cases. I basically write in there:

  • The interesting problems I had.
  • The decisions made.
  • How the implementation of the solution evolved overtime.
  • The possible mistakes we made along the way.

It’s a great resource when you have a problem and you want to look at your past experience.

To evaluate your decisions overtime, nothing will beat a good monitoring process: logs, tests, and so on. It’s what the book Building Evolutionary Architecture call “fitness functions” for example, some monitoring allowing you to measure how healthy your architecture stays overtime. It doesn’t have to stop to the architecture; you can think about different monitoring system to see how something evolve, especially if the solution has still a lot of uncertainty regarding its benefits, drawbacks, and risks.

You can also do that retrospectively: looking at how the code complexity evolve overtime using Git for example.

Retrospective on the Process

We defined the problem, implemented a solution iteratively, and now the problem is gone. That’s it! We made it! Are we done now?

Decisions are sometimes not optimal, and implementing a solution successfully doesn’t mean that there wasn’t a better (simpler) one to begin with. That’s why it can be beneficial to look back and understand what went right, and what went wrong. For example, we can ask ourselves these questions:

  • Looking at what we learned during the whole process, is there a potentially better hypothesis to solve the problem in a simpler, more robust way?
  • What are the benefits and drawbacks we missed when speaking about the different hypothesizes, but we discovered during the implementation? Why we didn’t think about them beforehand?
  • What other problems did we encounter during the implementation? Did we solve them? Did we differ some? What should be the next steps regarding these new problems?
  • What kind of monitoring did we put in place to make sure that the solution won’t have undesired outcomes overtime? Can we learn something with this data?

Reflecting on past solutions is a difficult thing to do. There is no way to logically assess that the decision taken was better than others, since we didn’t implement the other hypothesizes, and we didn’t look at them overtime to appreciate their consequences. But you can still look at the implementation of the solution overtime, and write in your developer journal each time there is a bug which seems directly related to the solution. Would the bugs be the same if another solution would had been applied?

Bugs are often not an option; they will pop up, eventually. Nonetheless, it’s important to make sure that you can fix them in a reasonable amount of time, and that you don’t see them creeping back in the codebase after being solved. Some metrics, from the DevOps movement (like MTTR for example) can help here. Sometimes, bugs will show you a better, more refined solution to the original problem; after all, bugs can also give you some useful information. They are also the most direct result of the implementation of your solution.

If you want to know more about measuring complexity (which can be also used to measure complexity overtime after applying a solution), I wrote a couple of articles on the subject .

Humility in Problem-Solving

It’s time to do a little summary. What did we see in this article?

  • We need to ensure that the problem we found is really a problem we need to solve. Is there any value to solve the problem? Is it even a problem?
  • Try to determine what kind of problem you have: a problem which can have multiple, specific, known answers (like a technical problem), or a problem which depends on the real-life context, without known solutions?
  • Defining the problem is important. Try to define it using different words. Write these definitions down. Does everybody in your team understand the problem equally?
  • It’s time to explore the solution space. Draft a couple of hypothesizes, their benefits, drawbacks, and risks. You can also do some prototyping if you think it would give you more information to take the best decision.
  • Do you have enough information to implement a hypothesis, becoming effectively a solution? If it’s not the case, it might be better to keep the status quo and try to solve the problem later, when you’ll have more information. But don’t forget the problem!
  • If you decide to implement a solution, do it step by step, especially if you’re unsure about the consequences of your decisions. Implement an independent part of the hypothesis, look at the consequences, adjust if necessary, and re-iterate.
  • When the solution is implemented, it’s time to reflect on the whole process: did we solve the problem? What other problems did we encounter? Maybe another solution would have been better? Why?

As I was writing above, most problems you’ll encounter will be complex ones, embedded into a changing environment with different moving parts. As a result, it’s difficult to train to solve problems in a vacuum; the only good training I know is solving real life problems. That’s why your experience is so important.

Experience build your intuition, which in turn increase your expertise.

You’ll never have 100% certainty that a solution will bring you the desired outcome, especially if you are in front of a complex problem with a blurry context. If you are absolutely convinced that you have the good solution without even beginning to implement it, I’d advise you to stay humber in front of the Gods of Complexity, or they will show you how little you know.

  • How to solve it
  • Hammock Driven Development
  • When Deferring Decisions Leads to Better Codebases
  • Lean Development - deferring decision

problem solving approach in programming

Member-only story

10 Steps to Solving a Programming Problem

Tips for new developers staring at a blank screen, unsure of where to start.

Valinda Chan

Valinda Chan

Some of the feedback I hear from new developers working on a programming problem revolves around uncertainty of where to start. You understand the problem, the logic, basics of the syntax, etc. If you see someone else’s code or have someone to guide you, you can follow along. But maybe you feel uncertain about doing it yourself and have trouble turning your thoughts into code at first even though you understand the syntax or logic. Here’s my process and some tips to tackling a sample problem that hopefully some of you may find helpful in your journey.

1. Read the problem at least three times (or however many makes you feel comfortable)

You can’t solve a problem you don’t understand. There is a difference between the problem and the problem you think you are solving. It’s easy to start reading the first few lines in a problem and assume the rest of it because it’s similar to something you’ve seen in the past. If you are making even a popular game like Hangman, be sure to read through any rules even if you’ve played it before. I once was asked to make a game like Hangman that I realized was “Evil Hangman” only after I read through the instructions (it was a trick!).

Sometimes I’ll even try explaining the problem to a friend and see if her understanding of my explanation matches the problem I am tasked with. You don’t want to find out halfway through that you misunderstood the problem. Taking extra time in the beginning is worth it. The better you understand the problem, the easier it will be to solve it.

Let’s pretend we are creating a simple function selectEvenNumbers that will take in an array of numbers and return an array evenNumbers of only even numbers. If there are no even numbers, return the empty array evenNumbers .

Here are some questions that run through my mind:

  • How can a computer tell what is an even number? Divide that number by 2 and see if its remainder is 0.
  • What am I passing into this function? An array
  • What will that array contain? One or more numbers
  • What are the data types of the elements in the array? Numbers
  • What is the goal of this function? What am I returning at the end of this function? The goal is to take all the even numbers and return them in an array. If there are no even numbers, return an empty array.

2. Work through the problem manually with at least three sets of sample data

Take out a piece of paper and work through the problem manually. Think of at least three sets of sample data you can use. Consider corner and edge cases as well.

Corner case : a problem or situation that occurs outside of normal operating parameters, specifically when multiple environmental variables or conditions are simultaneously at extreme levels, even though each parameter is within the specified range for that parameter. Edge case : problem or situation that occurs only at an extreme (maximum or minimum) operating parameter

For example, below are some sets of sample data to use:

When you are first starting out, it is easy to gloss over the steps. Because your brain may already be familiar with even numbers, you may just look at a sample set of data and pull out numbers like 2 , 4 , 6 and so forth in the array without fully being aware of each and every step your brain is taking to solve it. If this is challenging, try using large sets of data as it will override your brain’s ability to naturally solve the problem just by looking at it. That helps you work through the real algorithm.

Let’s go through the first array [1]

  • Look at the only element in the array [1]
  • Decide if it is even. It is not
  • Notice that there are no more elements in this array
  • Determine there are no even numbers in this provided array
  • Return an empty array

Let’s go through the array [1, 2]

  • Look at the first element in array [1, 2]
  • Look at the next element in the array
  • Decide if it is even. It is even
  • Make an array evenNumbers and add 2 to this array
  • Return the array evenNumbers which is [2]

I go through this a few more times. Notice how the steps I wrote down for [1] varies slightly from [1, 2] . That is why I try to go through a couple of different sets. I have some sets with just one element, some with floats instead of just integers, some with multiple digits in an element, and some with negatives just to be safe.

3. Simplify and optimize your steps

Look for patterns and see if there’s anything you can generalize. See if you can reduce any steps or if you are repeating any steps.

  • Create a function selectEvenNumbers
  • Create a new empty array evenNumbers where I store even numbers, if any
  • Go through each element in the array [1, 2]
  • Find the first element
  • Decide if it is even by seeing if it is divisible by 2. If it is even, I add that to evenNumbers
  • Find the next element
  • Repeat step #4
  • Repeat step #5 and #4 until there are no more elements in this array
  • Return the array evenNumbers , regardless of whether it has anything in it

This approach may remind you of Mathematical Induction in that you:

  • Show it is true for n = 1 , n = 2 , ...
  • Suppose it is true for n = k
  • Prove it is true for n = k + 1

4. Write pseudocode

Even after you’ve worked out general steps, writing out pseudocode that you can translate into code will help with defining the structure of your code and make coding a lot easier. Write pseudocode line by line. You can do this either on paper or as comments in your code editor. If you’re starting out and find blank screens to be daunting or distracting, I recommend doing it on paper.

Pseudocode generally does not actually have specific rules in particular but sometimes, I might end up including some syntax from a language just because I am familiar enough with an aspect of the programming language. Don’t get caught up with the syntax. Focus on the logic and steps.

For our problem, there are many different ways to do this. For example, you can use filter but for the sake of keeping this example as easy to follow along as possible, we will use a basic for loop for now (but we will use filter later when we refactor our code).

Here is an example of pseudocode that has more words:

Here is an example of pseudocode that has fewer words:

Either way is fine as long as you are writing it out line-by-line and understand the logic on each line.

Refer back to the problem to make sure you are on track.

5. Translate pseudocode into code and debug

When you have your pseudocode ready, translate each line into real code in the language you are working on. We will use JavaScript for this example.

If you wrote it out on paper, type this up as comments in your code editor. Then replace each line in your pseudocode.

Then I call the function and give it some sample sets of data we used earlier. I use them to see if my code returns the results I want. You can also write tests to check if the actual output is equal to the expected output.

I generally use console.log() after each variable or line or so. This helps me check if the values and code are behaving as expected before I move on . By doing this, I catch any issues before I get too far. Below is an example of what values I would check when I am first starting out. I do this throughout my code as I type it out.

After working though each line of my pseudocode, below is what we end up with. // is what the line was in pseudocode. Text that is bolded is the actual code in JavaScript.

I get rid of the pseudocode to avoid confusion.

Sometimes new developers will get hung up with the syntax that it becomes difficult to move forward. Remember that syntax will come more naturally over time and there is no shame in referencing material for the correct syntax later on when coding.

6. Simplify and optimize your code

You’ve probably noticed by now that simplifying and optimizing are recurring themes.

“Simplicity is prerequisite for reliability.” — Edsger W. Dijkstra, Dutch computer scientist and early pioneer in many research areas of computing science

In this example, one way of optimizing it would be to filter out items from an array by returning a new array using filter . This way, we don’t have to define another variable evenNumbers because filter will return a new array with copies of elements that match the filter. This will not change the original array. We also don’t need to use a for loop with this approach. filter will go through each item, return either true , to have that element in the array, or false to skip it.

Simplifying and optimizing your code may require you to iterate a few times, identifying ways to further simplify and optimize code.

Here are some questions to keep in mind:

  • What are your goals for simplifying and optimizing? The goals will depend on your team’s style or your personal preference. Are you trying to condense the code as much as possible? Is the goal to make it the code more readable? If that’s the case, you may prefer taking that extra line to define the variable or compute something rather than trying to define and compute all in one line.
  • How else can you make the code more readable?
  • Are there any more extra steps you can take out?
  • Are there any variables or functions you ended up not even needing or using?
  • Are you repeating some steps a lot? See if you can define in another function.
  • Are there better ways to handle edge cases?
“Programs must be written for people to read, and only incidentally for machines to execute.” — Gerald Jay Sussman and Hal Abelson, Authors of “Structure and Interpretation of Computer Programs”

This step really should be throughout the process. Debugging throughout will help you catch any syntax errors or gaps in logic sooner rather than later. Take advantage of your Integrated Development Environment (IDE) and debugger. When I encounter bugs, I trace the code line-by-line to see if there was anything that did not go as expected. Here are some techniques I use:

  • Check the console to see what the error message says. Sometimes it’ll point out a line number I need to check. This gives me a rough idea of where to start, although the issue sometimes may not be at this line at all.
  • Comment out chunks or lines of code and output what I have so far to quickly see if the code is behaving how I expected. I can always uncomment the code as needed.
  • Use other sample data if there are scenarios I did not think of and see if the code will still work.
  • Save different versions of my file if I am trying out a completely different approach. I don’t want to lose any of my work if I end up wanting to revert back to it!
“The most effective debugging tool is still careful thought, coupled with judiciously placed print statements.” — Brian W. Kernighan, Computer Science Professor at Princeton University

8. Write useful comments

You may not always remember what every single line meant a month later. And someone else working on your code may not know either. That’s why it’s important to write useful comments to avoid problems and save time later on if you need to come back to it.

Stay away from comments such as:

// This is an array. Iterate through it.

// This is a variable

I try to write brief, high-level comments that help me understand what’s going on if it is not obvious. This comes in handy when I am working on more complex problems. It helps understand what a particular function is doing and why. Through the use of clear variable names, function names, and comments, you (and others) should be able to understand:

  • What is this code for?
  • What is it doing?

9. Get feedback through code reviews

Get feedback from your teammates, professors, and other developers. Check out Stack Overflow . See how others tackled the problem and learn from them. There are sometimes several ways to approach a problem. Find out what they are and you’ll get better and quicker at coming up with them yourself.

“No matter how slow you are writing clean code, you will always be slower if you make a mess.” — Uncle Bob Martin, Software Engineer and Co-author of the Agile Manifesto

10. Practice, practice, practice

Even experienced developers are always practicing and learning. If you get helpful feedback, implement it. Redo a problem or do similar problems. Keep pushing yourself. With each problem you solve, the better a developer you become. Celebrate each success and be sure to remember how far you’ve come. Remember that programming, like with anything, comes easier and more naturally with time.

“Take pride in how far you’ve come. Have faith in how far you can go. But don’t forget to enjoy the journey.” — Michael Josephson, Founder of Joseph and Edna Josephson Institute of Ethics

Thanks Gavin Stark

Valinda Chan

Written by Valinda Chan

Product & UX Design

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What exactly is a programming paradigm?

Any fool can write code that a computer can understand. Good programmers write code that humans can understand. ― Martin Fowler

When programming, complexity is always the enemy. Programs with great complexity, with many moving parts and interdependent components, seem initially impressive. However, the ability to translate a real-world problem into a simple or elegant solution requires a deeper understanding.

While developing an application or solving a simple problem, we often say “If I had more time, I would have written a simpler program”. The reason is, we made a program with greater complexity. The less complexity we have, the easier it is to debug and understand. The more complex a program becomes, the harder it is to work on it.

Managing complexity is a programmer’s main concern . So how do programmers deal with complexity? There are many general approaches that reduce complexity in a program or make it more manageable. One of the main approaches is a programming paradigm. Let's dive into programming paradigms!

Introduction to programming paradigms

The term programming paradigm refers to a style of programming . It does not refer to a specific language, but rather it refers to the way you program.

There are lots of programming languages that are well-known but all of them need to follow some strategy when they are implemented. And that strategy is a paradigm.

The types of programming paradigms

types-of-paradigms

Imperative programming paradigm

The word “imperative” comes from the Latin “impero” meaning “I command”.

It’s the same word we get “emperor” from, and that’s quite apt. You’re the emperor. You give the computer little orders to do and it does them one at a time and reports back.

The paradigm consists of several statements, and after the execution of all of them, the result is stored. It’s about writing a list of instructions to tell the computer what to do step by step.

In an imperative programming paradigm, the order of the steps is crucial, because a given step will have different consequences depending on the current values of variables when the step is executed.

To illustrate, let's find the sum of first ten natural numbers in the imperative paradigm approach.

Example in C:

In the above example, we are commanding the computer what to do line by line. Finally, we are storing the value and printing it.

1.1 Procedural programming paradigm

Procedural programming (which is also imperative) allows splitting those instructions into procedures .

NOTE: Procedures aren't functions. The difference between them is that functions return a value, and procedures do not. More specifically, functions are designed to have minimal side effects, and always produce the same output when given the same input. Procedures, on the other hand, do not have any return value. Their primary purpose is to accomplish a given task and cause a desired side effect.

A great example of procedures would be the well known for loop. The for loop's main purpose is to cause side effects and it does not return a value.

To illustrate, let's find the sum of first ten natural numbers in the procedural paradigm approach.

In the example above, we've used a simple for loop to find the summation of the first ten natural numbers.

Languages that support the procedural programming paradigm are:

Procedural programming is often the best choice when:

  • There is a complex operation which includes dependencies between operations, and when there is a need for clear visibility of the different application states ('SQL loading', 'SQL loaded', 'Network online', 'No audio hardware', etc). This is usually appropriate for application startup and shutdown (Holligan, 2016).
  • The program is very unique and few elements were shared (Holligan, 2016).
  • The program is static and not expected to change much over time (Holligan, 2016).
  • None or only a few features are expected to be added to the project over time (Holligan, 2016).

Why should you consider learning the procedural programming paradigm?

  • It's simple.
  • An easier way to keep track of program flow.
  • It has the ability to be strongly modular or structured.
  • Needs less memory: It's efficient and effective.

1.2 Object-oriented programming paradigm

OOP is the most popular programming paradigm because of its unique advantages like the modularity of the code and the ability to directly associate real-world business problems in terms of code.

Object-oriented programming offers a sustainable way to write spaghetti code. It lets you accrete programs as a series of patches. ― Paul Graham

The key characteristics of object-oriented programming include Class, Abstraction, Encapsulation, Inheritance and Polymorphism.

A class is a template or blueprint from which objects are created.

java-oops

Objects are instances of classes. Objects have attributes/states and methods/behaviors. Attributes are data associated with the object while methods are actions/functions that the object can perform.

oop

Abstraction separates the interface from implementation. Encapsulation is the process of hiding the internal implementation of an object.

Inheritance enables hierarchical relationships to be represented and refined. Polymorphism allows objects of different types to receive the same message and respond in different ways.

To illustrate, let's find the sum of first ten natural numbers in the object-oriented paradigm approach.

Example in Java:

We have a class Addition that has two states, sum and num which are initialized to zero. We also have a method addValues() which returns the sum of num numbers.

In the Main class, we've created an object, obj of Addition class. Then, we've initialized the num to 10 and we've called addValues() method to get the sum.

Languages that support the object-oriented paradigm:

Object-oriented programming is best used when:

  • You have multiple programmers who don’t need to understand each component (Holligan, 2016).
  • There is a lot of code that could be shared and reused (Holligan, 2016).
  • The project is anticipated to change often and be added to over time (Holligan, 2016).

Why should you consider learning the object-oriented programming paradigm?

  • Reuse of code through Inheritance.
  • Flexibility through Polymorphism.
  • High security with the use of data hiding (Encapsulation) and Abstraction mechanisms.
  • Improved software development productivity: An object-oriented programmer can stitch new software objects to make completely new programs (The Saylor Foundation, n.d.).
  • Faster development: Reuse enables faster development (The Saylor Foundation, n.d.).
  • Lower cost of development: The reuse of software also lowers the cost of development. Typically, more effort is put into the object-oriented analysis and design (OOAD), which lowers the overall cost of development (The Saylor Foundation, n.d.).
  • Higher-quality software: Faster development of software and lower cost of development allows more time and resources to be used in the verification of the software. Object-oriented programming tends to result in higher-quality software (The Saylor Foundation, n.d.).

1.3 Parallel processing approach

Parallel processing is the processing of program instructions by dividing them among multiple processors.

A parallel processing system allows many processors to run a program in less time by dividing them up.

Languages that support the Parallel processing approach:

  • NESL (one of the oldest ones)

Parallel processing approach is often the best use when:

  • You have a system that has more than one CPU or multi-core processors which are commonly found on computers today.
  • You need to solve some computational problems that take hours/days to solve even with the benefit of a more powerful microprocessor.
  • You work with real-world data that needs more dynamic simulation and modeling.

Why should you consider learning the parallel processing approach?

  • Speeds up performance.
  • Often used in Artificial Intelligence. Learn more here: Artificial Intelligence and Parallel Processing by Seyed H. Roosta.
  • It makes it easy to solve problems since this approach seems to be like a divide and conquer method.

Here are some useful resources to learn more about parallel processing:

  • Parallel Programming in C by Paul Gribble
  • Introduction to Parallel Programming with MPI and OpenMP by Charles Augustine
  • INTRODUCTION TO PARALLEL PROGRAMMING WITH MPI AND OPENMP by Benedikt Steinbusch

2. Declarative programming paradigm

Declarative programming is a style of building programs that expresses the logic of a computation without talking about its control flow.

Declarative programming is a programming paradigm in which the programmer defines what needs to be accomplished by the program without defining how it needs to be implemented. In other words, the approach focuses on what needs to be achieved instead of instructing how to achieve it.

Imagine the president during the state of the union declaring their intentions for what they want to happen. On the other hand, imperative programming would be like a manager of a McDonald's franchise. They are very imperative and as a result, this makes everything important. They, therefore, tell everyone how to do everything down to the simplest of actions.

So the main differences are that imperative tells you how to do something and declarative tells you what to do .

2.1 Logic programming paradigm

The logic programming paradigm takes a declarative approach to problem-solving. It's based on formal logic.

The logic programming paradigm isn't made up of instructions - rather it's made up of facts and clauses. It uses everything it knows and tries to come up with the world where all of those facts and clauses are true.

For instance, Socrates is a man, all men are mortal, and therefore Socrates is mortal.

The following is a simple Prolog program which explains the above instance:

The first line can be read, "Socrates is a man.'' It is a base clause , which represents a simple fact.

The second line can be read, "X is mortal if X is a man;'' in other words, "All men are mortal.'' This is a clause , or rule, for determining when its input X is "mortal.'' (The symbol ":-'', sometimes called a turnstile , is pronounced "if''.) We can test the program by asking the question:

that is, "Is Socrates mortal?'' (The " ?- '' is the computer's prompt for a question). Prolog will respond " yes ''. Another question we may ask is:

That is, "Who (X) is mortal?'' Prolog will respond " X = Socrates ''.

To give you an idea, John is Bill's and Lisa's father. Mary is Bill's and Lisa's mother. Now, if someone asks a question like "who is the father of Bill and Lisa?" or "who is the mother of Bill and Lisa?" we can teach the computer to answer these questions using logic programming.

Example in Prolog:

Example explained:

The above code defines that John is Bill's father.

We're asking Prolog what value of X makes this statement true? X should be Mary to make the statement true. It'll respond X = Mary

Languages that support the logic programming paradigm:

  • ALF (algebraic logic functional programming language)

Logic programming paradigm is often the best use when:

  • If you're planning to work on projects like theorem proving, expert systems, term rewriting, type systems and automated planning.

Why should you consider learning the logic programming paradigm?

  • Easy to implement the code.
  • Debugging is easy.
  • Since it's structured using true/false statements, we can develop the programs quickly using logic programming.
  • As it's based on thinking, expression and implementation, it can be applied in non-computational programs too.
  • It supports special forms of knowledge such as meta-level or higher-order knowledge as it can be altered.

2.2 Functional programming paradigm

The functional programming paradigm has been in the limelight for a while now because of JavaScript, a functional programming language that has gained more popularity recently.

The functional programming paradigm has its roots in mathematics and it is language independent. The key principle of this paradigm is the execution of a series of mathematical functions.

You compose your program of short functions. All code is within a function. All variables are scoped to the function.

In the functional programming paradigm, the functions do not modify any values outside the scope of that function and the functions themselves are not affected by any values outside their scope.

To illustrate, let's identify whether the given number is prime or not in the functional programming paradigm.

Example in JavaScript:

In the above example, we've used Math.floor() and Math.sqrt() mathematical functions to solve our problem efficiently. We can solve this problem without using built-in JavaScript mathematical functions, but to run the code efficiently it is recommended to use built-in JS functions.

number is scoped to the function isPrime() and it will not be affected by any values outside its scope. isPrime() function always produces the same output when given the same input.

NOTE: there are no for and while loops in functional programming. Instead, functional programming languages rely on recursion for iteration (Bhadwal, 2019).

Languages that support functional programming paradigm:

Functional programming paradigm is often best used when:

  • Working with mathematical computations.
  • Working with applications aimed at concurrency or parallelism.

Why should you consider learning the functional programming paradigm?

  • Functions can be coded quickly and easily.
  • General-purpose functions can be reusable which leads to rapid software development.
  • Unit testing is easier.
  • Debugging is easier.
  • Overall application is less complex since functions are pretty straightforward.

2.3 Database processing approach

This programming methodology is based on data and its movement. Program statements are defined by data rather than hard-coding a series of steps.

A database is an organized collection of structured information, or data, typically stored electronically in a computer system. A database is usually controlled by a database management system (DBMS) ("What is a Database", Oracle, 2019).

To process the data and querying them, databases use tables . Data can then be easily accessed, managed, modified, updated, controlled and organized.

A good database processing approach is crucial to any company or organization. This is because the database stores all the pertinent details about the company such as employee records, transaction records and salary details.

Most databases use Structured Query Language (SQL) for writing and querying data.

Here’s an example in database processing approach (SQL):

The PersonID column is of type int and will hold an integer. The LastName , FirstName , Address , and City columns are of type varchar and will hold characters, and the maximum length for these fields is 255 characters.

The empty Persons table will now look like this:

Screenshot-from-2019-11-10-22-37-53

Database processing approach is often best used when:

  • Working with databases to structure them.
  • Accessing, modifying, updating data on the database.
  • Communicating with servers.

Why are databases important and why should you consider learning database processing approach?

  • Massive amount of data is handled by the database: Unlike spreadsheet or other tools, databases are used to store large amount of data daily.
  • Accurate: With the help of built-in functionalities in a database, we can easily validate.
  • Easy to update data: Data Manipulation Languages (DML) such as SQL are used to update data in a database easily.
  • Data integrity: With the help of built-in validity checks, we can ensure the consistency of data.

Programming paradigms reduce the complexity of programs. Every programmer must follow a paradigm approach when implementing their code. Each one has its advantages and disadvantages .

If you're a beginner, I would like to suggest learning object-oriented programming and functional programming first. Understand their concepts and try to apply them in your projects.

For example, if you're learning object-oriented programming, the pillars of object-oriented programming are Encapsulation, Abstraction, Inheritance and Polymorphism. Learn them by doing it. It will help you to understand their concepts on a deeper level, and your code will be less complex and more efficient and effective.

I strongly encourage you to read more related articles on programming paradigms. I hope this article helped you.

Please feel free to let me know if you have any questions.

You can contact and connect with me on Twitter @ThanoshanMV .

Thank you for reading.

Happy Coding!

  • Akhil Bhadwal. (2019). Functional Programming: Concepts, Advantages, Disadvantages, and Applications
  • Alena Holligan. (2016). When to use OOP over procedural coding
  • The Saylor Foundation. (n.d.). Advantages and Disadvantages of Object-Oriented Programming (OOP)
  • What is a Database | Oracle. (2019).

System.out.println("Hey there, I am Thanoshan!");

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Problem Solving

Foundations course, introduction.

Before we start digging into some pretty nifty JavaScript, we need to begin talking about problem solving : the most important skill a developer needs.

Problem solving is the core thing software developers do. The programming languages and tools they use are secondary to this fundamental skill.

From his book, “Think Like a Programmer” , V. Anton Spraul defines problem solving in programming as:

Problem solving is writing an original program that performs a particular set of tasks and meets all stated constraints.

The set of tasks can range from solving small coding exercises all the way up to building a social network site like Facebook or a search engine like Google. Each problem has its own set of constraints, for example, high performance and scalability may not matter too much in a coding exercise but it will be vital in apps like Google that need to service billions of search queries each day.

New programmers often find problem solving the hardest skill to build. It’s not uncommon for budding programmers to breeze through learning syntax and programming concepts, yet when trying to code something on their own, they find themselves staring blankly at their text editor not knowing where to start.

The best way to improve your problem solving ability is by building experience by making lots and lots of programs. The more practice you have the better you’ll be prepared to solve real world problems.

In this lesson we will walk through a few techniques that can be used to help with the problem solving process.

Lesson overview

This section contains a general overview of topics that you will learn in this lesson.

  • Explain the three steps in the problem solving process.
  • Explain what pseudocode is and be able to use it to solve problems.
  • Be able to break a problem down into subproblems.

Understand the problem

The first step to solving a problem is understanding exactly what the problem is. If you don’t understand the problem, you won’t know when you’ve successfully solved it and may waste a lot of time on a wrong solution .

To gain clarity and understanding of the problem, write it down on paper, reword it in plain English until it makes sense to you, and draw diagrams if that helps. When you can explain the problem to someone else in plain English, you understand it.

Now that you know what you’re aiming to solve, don’t jump into coding just yet. It’s time to plan out how you’re going to solve it first. Some of the questions you should answer at this stage of the process:

  • Does your program have a user interface? What will it look like? What functionality will the interface have? Sketch this out on paper.
  • What inputs will your program have? Will the user enter data or will you get input from somewhere else?
  • What’s the desired output?
  • Given your inputs, what are the steps necessary to return the desired output?

The last question is where you will write out an algorithm to solve the problem. You can think of an algorithm as a recipe for solving a particular problem. It defines the steps that need to be taken by the computer to solve a problem in pseudocode.

Pseudocode is writing out the logic for your program in natural language instead of code. It helps you slow down and think through the steps your program will have to go through to solve the problem.

Here’s an example of what the pseudocode for a program that prints all numbers up to an inputted number might look like:

This is a basic program to demonstrate how pseudocode looks. There will be more examples of pseudocode included in the assignments.

Divide and conquer

From your planning, you should have identified some subproblems of the big problem you’re solving. Each of the steps in the algorithm we wrote out in the last section are subproblems. Pick the smallest or simplest one and start there with coding.

It’s important to remember that you might not know all the steps that you might need up front, so your algorithm may be incomplete -— this is fine. Getting started with and solving one of the subproblems you have identified in the planning stage often reveals the next subproblem you can work on. Or, if you already know the next subproblem, it’s often simpler with the first subproblem solved.

Many beginners try to solve the big problem in one go. Don’t do this . If the problem is sufficiently complex, you’ll get yourself tied in knots and make life a lot harder for yourself. Decomposing problems into smaller and easier to solve subproblems is a much better approach. Decomposition is the main way to deal with complexity, making problems easier and more approachable to solve and understand.

In short, break the big problem down and solve each of the smaller problems until you’ve solved the big problem.

Solving Fizz Buzz

To demonstrate this workflow in action, let’s solve a common programming exercise: Fizz Buzz, explained in this wiki article .

Understanding the problem

Write a program that takes a user’s input and prints the numbers from one to the number the user entered. However, for multiples of three print Fizz instead of the number and for the multiples of five print Buzz . For numbers which are multiples of both three and five print FizzBuzz .

This is the big picture problem we will be solving. But we can always make it clearer by rewording it.

Write a program that allows the user to enter a number, print each number between one and the number the user entered, but for numbers that divide by 3 without a remainder print Fizz instead. For numbers that divide by 5 without a remainder print Buzz and finally for numbers that divide by both 3 and 5 without a remainder print FizzBuzz .

Does your program have an interface? What will it look like? Our FizzBuzz solution will be a browser console program, so we don’t need an interface. The only user interaction will be allowing users to enter a number.

What inputs will your program have? Will the user enter data or will you get input from somewhere else? The user will enter a number from a prompt (popup box).

What’s the desired output? The desired output is a list of numbers from 1 to the number the user entered. But each number that is divisible by 3 will output Fizz , each number that is divisible by 5 will output Buzz and each number that is divisible by both 3 and 5 will output FizzBuzz .

Writing the pseudocode

What are the steps necessary to return the desired output? Here is an algorithm in pseudocode for this problem:

Dividing and conquering

As we can see from the algorithm we developed, the first subproblem we can solve is getting input from the user. So let’s start there and verify it works by printing the entered number.

With JavaScript, we’ll use the “prompt” method.

The above code should create a little popup box that asks the user for a number. The input we get back will be stored in our variable answer .

We wrapped the prompt call in a parseInt function so that a number is returned from the user’s input.

With that done, let’s move on to the next subproblem: “Loop from 1 to the entered number”. There are many ways to do this in JavaScript. One of the common ways - that you actually see in many other languages like Java, C++, and Ruby - is with the for loop :

If you haven’t seen this before and it looks strange, it’s actually straightforward. We declare a variable i and assign it 1: the initial value of the variable i in our loop. The second clause, i <= answer is our condition. We want to loop until i is greater than answer . The third clause, i++ , tells our loop to increment i by 1 every iteration. As a result, if the user inputs 10, this loop would print numbers 1 - 10 to the console.

Most of the time, programmers find themselves looping from 0. Due to the needs of our program, we’re starting from 1

With that working, let’s move on to the next problem: If the current number is divisible by 3, then print Fizz .

We are using the modulus operator ( % ) here to divide the current number by three. If you recall from a previous lesson, the modulus operator returns the remainder of a division. So if a remainder of 0 is returned from the division, it means the current number is divisible by 3.

After this change the program will now output this when you run it and the user inputs 10:

The program is starting to take shape. The final few subproblems should be easy to solve as the basic structure is in place and they are just different variations of the condition we’ve already got in place. Let’s tackle the next one: If the current number is divisible by 5 then print Buzz .

When you run the program now, you should see this output if the user inputs 10:

We have one more subproblem to solve to complete the program: If the current number is divisible by 3 and 5 then print FizzBuzz .

We’ve had to move the conditionals around a little to get it to work. The first condition now checks if i is divisible by 3 and 5 instead of checking if i is just divisible by 3. We’ve had to do this because if we kept it the way it was, it would run the first condition if (i % 3 === 0) , so that if i was divisible by 3, it would print Fizz and then move on to the next number in the iteration, even if i was divisible by 5 as well.

With the condition if (i % 3 === 0 && i % 5 === 0) coming first, we check that i is divisible by both 3 and 5 before moving on to check if it is divisible by 3 or 5 individually in the else if conditions.

The program is now complete! If you run it now you should get this output when the user inputs 20:

  • Read How to Think Like a Programmer - Lessons in Problem Solving by Richard Reis.
  • Watch How to Begin Thinking Like a Programmer by Coding Tech. It’s an hour long but packed full of information and definitely worth your time watching.
  • Read this Pseudocode: What It Is and How to Write It article from Built In.

Knowledge check

This section contains questions for you to check your understanding of this lesson on your own. If you’re having trouble answering a question, click it and review the material it links to.

  • What are the three stages in the problem solving process?
  • Why is it important to clearly understand the problem first?
  • What can you do to help get a clearer understanding of the problem?
  • What are some of the things you should do in the planning stage of the problem solving process?
  • What is an algorithm?
  • What is pseudocode?
  • What are the advantages of breaking a problem down and solving the smaller problems?

Additional resources

This section contains helpful links to other content. It isn’t required, so consider it supplemental.

  • Read the first chapter in Think Like a Programmer: An Introduction to Creative Problem Solving ( not free ). This book’s examples are in C++, but you will understand everything since the main idea of the book is to teach programmers to better solve problems. It’s an amazing book and worth every penny. It will make you a better programmer.
  • Watch this video on repetitive programming techniques .
  • Watch Jonathan Blow on solving hard problems where he gives sage advice on how to approach problem solving in software projects.

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Introduction of Programming Paradigms

Paradigm can also be termed as method to solve some problem or do some task. Programming paradigm is an approach to solve problem using some programming language or also we can say it is a method to solve a problem using tools and techniques that are available to us following some approach. There are lots for programming language that are known but all of them need to follow some strategy when they are implemented and this methodology/strategy is paradigms. Apart from varieties of programming language there are lots of paradigms to fulfill each and every demand. They are discussed below:

problem solving approach in programming

1. Imperative programming paradigm:  It is one of the oldest programming paradigm. It features close relation to machine architecture. It is based on Von Neumann architecture. It works by changing the program state through assignment statements. It performs step by step task by changing state. The main focus is on how to achieve the goal. The paradigm consist of several statements and after execution of all the result is stored.

Advantages:  

  • Very simple to implement
  • It contains loops, variables etc.

Disadvantage:   

  • Complex problem cannot be solved
  • Less efficient and less productive
  • Parallel programming is not possible

Imperative programming is divided into three broad categories: Procedural, OOP and parallel processing. These paradigms are as follows:

  • Procedural programming paradigm –   This paradigm emphasizes on procedure in terms of under lying machine model. There is no difference in between procedural and imperative approach. It has the ability to reuse the code and it was boon at that time when it was in use because of its reusability.

Then comes OOP,

  • Object oriented programming –   The program is written as a collection of classes and object which are meant for communication. The smallest and basic entity is object and all kind of computation is performed on the objects only. More emphasis is on data rather procedure. It can handle almost all kind of real life problems which are today in scenario.
  • Data security
  • Inheritance
  • Code reusability
  • Flexible and abstraction is also present
  • Parallel processing approach –   Parallel processing is the processing of program instructions by dividing them among multiple processors. A parallel processing system posses many numbers of processor with the objective of running a program in less time by dividing them. This approach seems to be like divide and conquer. Examples are NESL (one of the oldest one) and C/C++ also supports because of some library function.

2. Declarative programming paradigm:   It is divided as Logic, Functional, Database. In computer science the declarative programming is a style of building programs that expresses logic of computation without talking about its control flow. It often considers programs as theories of some logic.It may simplify writing parallel programs. The focus is on what needs to be done rather how it should be done basically emphasize on what code is actually doing. It just declares the result we want rather how it has be produced. This is the only difference between imperative (how to do) and declarative (what to do) programming paradigms. Getting into deeper we would see logic, functional and database.

  • Logic programming paradigms –   It can be termed as abstract model of computation. It would solve logical problems like puzzles, series etc. In logic programming we have a knowledge base which we know before and along with the question and knowledge base which is given to machine, it produces result. In normal programming languages, such concept of knowledge base is not available but while using the concept of artificial intelligence, machine learning we have some models like Perception model which is using the same mechanism.  In logical programming the main emphasize is on knowledge base and the problem. The execution of the program is very much like proof of mathematical statement, e.g., Prolog
  • Functional programming paradigms –   The functional programming paradigms has its roots in mathematics and it is language independent. The key principle of this paradigms is the execution of series of mathematical functions. The central model for the abstraction is the function which are meant for some specific computation and not the data structure. Data are loosely coupled to functions.The function hide their implementation. Function can be replaced with their values without changing the meaning of the program. Some of the languages like perl, javascript mostly uses this paradigm.

The next kind of approach is of Database.

  • Database/Data driven programming approach –   This programming methodology is based on data and its movement. Program statements are defined by data rather than hard-coding a series of steps. A database program is the heart of a business information system and provides file creation, data entry, update, query and reporting functions. There are several programming languages that are developed mostly for database application. For example SQL. It is applied to streams of structured data, for filtering, transforming, aggregating (such as computing statistics), or calling other programs. So it has its own wide application.

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problem solving approach in programming

The Beginner Programmer's guide to Problem Solving [With Example]

problem solving approach in programming

Have you got this feeling that you are able to grasp the concepts of programming and  you are able to understand what’s a variable, what’s a function, what are data types, etc. yet you find it difficult to solve problems in programming.  Every beginner gets this feeling.  I did too when starting out.

It is important to overcome this feeling at the earliest, otherwise it can form a mental block for you.

Image 1

How it can be a mental block to you?  Common sense says that the more you practice a certain skill, you get better at that skill as time progresses.  Same goes with problem solving too.  The more problems you solve, the better you become at problem solving.  But when you get a feel that you are trying hard and still unable to solve a problem or find it extremely difficult, your confidence lowers.  At this stage, either you stop solving problems or try to solve lesser number of problems.

The point is your curriculum or your professional work is generally designed in such a manner that the order of difficulty increases as time progresses.  So, you are in a situation where you feel less confident in solving small problems but now tasked with solving bigger problems.  And the cycle continues till it becomes a permanent mental block in you.

Is it too late to start solving problems?

No.  If you have come to the realization that you need to improve your problem solving skills, you have made that good first step.  Quite often our egos don’t let us accept the obvious.  It is good to accept certain truth because that is the only way that we can improve ourselves.

What can I do to become better at solving problems?

Remove the mental block first – exercise your mind.

Your mind is your most powerful weapon.  So you have to think you can actually solve the problem.  So from today, think positively that you can solve any problem.  But you will obviously start with small problems and go on to solve bigger problems.

As with every aspect in life, it starts with conditioning the mind.  So, starting today, tell yourselves the following:

  • I can solve any problem that is put at me
  • I will commit at least 1-2 hours per day on solving problems alone for the next 30 days
  • I will never give up on any problem that is put at me, I will ask for help if required.1

Understand the basic approach to problem solving

Do you know one of the reasons for your struggle with problem solving?  One reason might be due to lack of practice.  But the main reason is because you have not understood the basics of problem solving especially in programming.  Once you understand the approach to problem solving to the smallest of things, you can go ahead and solve bigger and more complex problems with confidence.1

Ever wondered how top tech companies like Google, Amazon solved the internet’s biggest & hardest problems?  The answer is simplicity.  They solved problems at the basic level and then went on to solve bigger and bigger problems.  You can do it too.  But you need to be good at the basics.

What do I need to understand before even trying to solve the problem?

Understand the problem clearly – the power of clarity.

You need to understand your problem clearly before even trying to solve it1.  Lack of clarity at this stage will put you down.  So make a conscious effort in understanding the problem more clearly.  Ask questions like What, Why, When, Where, What if and How.  Not all questions might be applicable to your problem, but it is important to ask questions to yourself at this stage before you go ahead trying to solve the problem.

Visualize – The Power of visualization

I am sure everyone of you is aware of what visualization is.  Trying to picturize your thoughts.  Have you ever imagined how some people can solve extra ordinary problems just by looking into those problems and they will instantly have a solution to it?  And we don’t even understand the problem fully?  It is because they do it with their mind.  They visualize the problem in their minds and they solve it in their minds itself.  Visualization is a powerful tool in your mind.

But in order to get to that state, first you need to visualize the problem externally.  That is where a pen and a paper/notebook (or) a white board comes into play1.  Try to visualize the problem at hand and try to picturize the problem.  That is also one of the steps to make sure that you understand the problem clearly.

There was a situation when I and my dear friend & colleague were discussing about a problem and we were literally going nowhere.  This was actually when we each had around 7 years of experience in the industry.  At that point, my friend said “Let’s put our points in board.  If we don’t put it on the board, we will never get started”.  And we started putting things on board.  Things started to get more clear and raised more questions and ultimately became more clear.

That is the power of visualization.  It really helps us to get started with our thinking. This visual thing works.  Just try it out.

Your next question might be “I kinda get it, but I don’t.  How do I visualize? What exactly do I visualize?”.  Please read on to find out the answers.

What is the basic approach to problem solving

Step 1:  identify small problems.

The major trick in problem solving is to identify and solve the smallest problem and then moving ahead with bigger ones.  So how do you do it?

The answer is division of responsibility.  Simply put, we need to identify parts that can stand on its own and identify a sequence in those responsibilities.  And once you start breaking down the problems into smaller ones, then you can go ahead with the next step.

Step 2:  Solve the smaller problems one at a time

Now that you have identified the smaller problems, try to solve them.  While solving them, make sure that you are focussing only on one problem at a time.  That makes life much simpler for us.  If you feel that this smaller problem is too big to solve on its own, try to break it down further.  You need to iterate steps 1 to step 3 for each smaller problem.  But for now, ignore the bigger problem and solve the rest of the problems.

  • It is ok to assume that other problems are solved
  • It is ok to hardcode when coding a particular problem, but later you will resolve it in step 3.
  • Solve the easier problems first, that will give you confidence and momentum until you get the confidence to solve the hardest problem first.

Step 3: Connect the dots (Integration)

You have solved individual problems.  Now it is time to connect the dots by connecting the individual solution.  Identify those steps which will make the solution or the program complete.  Typically in programming, the dots are connected by passing data that is stored in variables.

Step 4: Try to optimize each step & across steps

Once you are completed with a working solution, try to optimize the solution with the best code that you can write.  This comes only with practice.  This trick can make a difference between a good programmer and a great programmer.  But to get to this step, you need to be first good at steps 1 to 3.

Let’s take an example & walkthrough the problem solving approach

Problem:  check if a user given string is a palindrome or not.

I will be using Python for this exercise (Although I have experience in1 C# and JAVA, I am also a Python beginner, so pardon any bad code).  Let’s iterate through our steps:

Let’s call this as Level 1:

Step 1:  Identify smaller problems:

Image 2

Step 2: Solve the small problems

So each small problem will map to its corresponding solution as below:

Image 3

Note: When solving the step (3.  Compare the variables), I am doing 2 things:

  • I am making an assumption that reversed is the variable name of the reversed string.
  • I am hardcoding the variable name reversed to ‘madam’ to avoid compile time error
  • If you execute the program at this state, you can input ‘madam’ and check if it is printing ‘The given string is a palindrome’ (And) you can input something else like ‘dog’ and check if it is printing ‘The given string is not a palindrome’

When we are trying to connect the dots, the only thing that is missing now is the variable reversed is hardcoded.  For that to be set to the correct value, we need to break the small problem (Reverse the user input and store in a separate variable) into further smaller problems.  Till that point we need to mark it as incomplete.

2 things still remain unsolved in Level 1:

  • Solution for step 2 in the diagram (Reverse the user input and store in a separate variable)
  • Connecting the dots once the solution for step 2 is found

Iterating small problem 2 through our problem solving steps:

Let’s call this Level 2:

Step 1: Identify smaller problems

Image 4

Step 3: Connect the dots

Here, we have already connected the dots.  So we need not do anything extra in this step.

Now we have solved the smaller problems, which means Level 2 is over.  Now we need to come back to Level 1.

If you remember, 2 things remain in Level 1.  One is solution for step 2 which we have found now.  Two is connecting the dots.

Now if we substitute the small problem 2 with the solution that we derived just now, we get something like this:

Image 6

The thing that remains is connecting the dots.

So if we see what is the missing connection, the variable reversed is set twice.  One to the solution of step 2 and another is hardcoded in step 3.  So we can now remove the hardcoded value in step 3, in which case our code will become like this

Image 7

If you see, we have actually solved our problem.

We are left with step 4 – Optimize each step and across steps

Step 4: Try to optimize each step and across steps

As you can see, there are many things that needs to be optimized for this code.  I would leave you to optimize the code further.  Come on, put on your thinking cap and try different solutions.

BONUS STEP 5:  Make the code robust

By robust I mean,

  • Adding error & exception handling
  • Using better variable names
  • Adding user defined functions
  • Adding comments where necessary

Again, I would leave you to figure out how to do this step.

  • We saw just how we can solve problems using a step by step approach
  • By solving smaller problems, I get into a momentum for solving bigger & tougher problems
  • By focussing one problem at a time, I am eliminating distractions, thus allowing to better direct your efforts for that one problem rather than getting confused with many small problems at hand.
  • If you understand this approach and practice, you will definitely go on to solve bigger problems and your confidence will raise.
  • Beauty about breaking down the problem is that we can further convert each problem and sub problem into separate functions/modules thus making the code more modularized and maintainable.

Wait, You can’t leave yet:

Now dear beginner programmers, take any problem and try to apply this approach.  See the results for yourselves.  Now, describe the following in the comments section:

  • What problem you are solving?
  • How did you break it down? (Even a snap of your notebook page or board will do!)
  • The final code
  • How did you feel and what did you learn from this exercise?

Also remember, I am challenging you for the 30 day problem solving challenge.

If you liked this blog post, please feel free to share it with your circles in social media.

This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)

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7 Tips on How to Approach and Solve Coding Problems Successfully

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If you hope to excel at writing code and land the job of your dreams, you should begin with building your problem-solving abilities.

tips on how to solve coding problems in interviews

Proficiency in software development is often associated with the ability to write complex codes in a short span of time. However, in practice, the field is as much about critical analysis and communication as it is about tackling coding challenges quickly and efficiently. At its core, software programming essentially means to solve problems. It teaches you to think analytically and determine the best possible solution to a problem — something that is common among all successful software developers. 

So, if you hope to excel at writing code and land the job of your dreams, you should begin with building your problem-solving abilities. In this article, we will provide you with 7 proven tips to approach problems so you get better at coding. So, let’s get started with the first one!

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1. get out of your comfort zone.

You should be well-versed with the underlying logic of algorithms so as to determine optimal solutions to coding problems. So, begin with brushing up on basic data structures and algorithms, and developing in-depth understanding of core conceits. It is recommended that you take it slow; increase the difficulty level of your learning and coding gradually. In any case, do not directly jump onto something advanced without learning the basics. 

You should also ensure that you don't get comfortable with executing codes using just one approach. The idea is to familiarize yourself with different problem-solving techniques to build your coding skills. One way to do this is to solve a variety of coding problems on challenging coding platforms like HackerRank, HackerEarth, Codewars, GitHub, to name a few. This will force you to be flexible, and employ the best possible framework for every problem.

Watch these videos

2. break the problem into parts.

It helps if you break the large and complex problem into sections and address them part by part. This will help you write simple and efficient codes devoid of any unnecessary complexities. 

Here's what you can do:

  • Create a flow chart illustrating the problem.
  • Break it into subproblems or smaller sections.
  • Solve each section by creating independent functions.
  • Arrange the functions in the order you need, thereby merging them all for the final solution. 
  • In case of errors, you can debug the code and search for an alternate approach. 

You can draw up the pseudo code and run it logically before replacing it with the original code. A pseudocode provides clarity on the requirements and functioning of the code, and thus makes writing the actual code easier. So, don't make the mistake of skipping this step. 

Read these articles

3. optimize your code for efficiency.

While there are a ton of programming algorithms and data structures to help you write codes, you should be able to choose the right software for optimised coding; hardware can not compensate for inefficiencies that arise due to incorrect algorithms and data structures. 

This is where time and space complexity, and stability come into the picture. For instance, the selection sort may not be the recommended for larger lists, it is highly efficient to sort smaller lists. Similarly, binary search is considered to be the quickest and most efficient search algorithm; however, linear search or breadth-first search also have their distinct advantages. 

So, given a problem, ask yourself, which sorting method is best suited for your input? Is it possible to eliminate nested loops? Does this search method provide a performance advantage? 

Examine the solution code thoroughly while simultaneously paying close attention to the time and space complexity. Determine if arrays, strings, binary trees, sorting, or loops are best suited to attain your goal. 

Run your code through edge cases by feeding empty or unacceptable inputs so you become aware of boundary conditions. LeetCode is an excellent platform when looking for coding problems to solve optimally. 

4. Learn from the past

Simply finding a solution to a coding problem should not be your primary goal; you should actively revisit your old codes to incorporate new concepts you've learned, eliminate redundant processes, if any, and optimize your code for performance and efficiency, thereby laying the groundwork for improvement. Ask yourself: 

  • Does the solution code run for all possible inputs?
  • Are there any alternate solutions  to the problem code?
  • How do I improve the performance and efficiency of the code? Is it possible to reduce the code space and compilation time? 
  • How can I make the code more readable? What are the redundant steps that can be struck out?

As you familiarize yourself with useful patterns or problems you faced in the past while solving coding questions, you'll succeed in building yourself a toolkit of solutions to apply to new problems. Learning about the flaws in your code helps you grow as a developer. 

5. Let feedback guide you towards improvement

Sometimes, all you need is a fresh set of your eyes to realign your coding perspective. So, don't hesitate to seek help from friends or colleagues if you are facing problems with your code. Your peers may have a better or more optimized framework that you aren't aware of. Allow them to teach you so as to hone your problem solving with algorithms and data structures. This is your best chance to refine your methodologies and get better at coding problems.

Make sure you return the favour if the opportunity ever presents itself. In this manner, you can collectively progress your coding abilities. 

6. Resist the temptation to look up for solution

If you are stuck somewhere, look up the solution code only as a last resort. When you do, you are likely to find a specific framework to solve the problem which in turn will serve to disrupt your individualistic problem-solving abilities. So, resist the temptation to look up the solution wherever possible. Think hard and try to arrive at the solution on your own. 

Even if you do go down that road, such as by using a tool like chatgpt , avoid memorizing or taking it down as is; simply familiarize yourself with the logic and attempt to create a new solution based on your learning. 

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Introduction to Programming with Java: A Problem Solving Approach

Introduction to Programming with Java: A Problem Solving Approach , 3rd Edition

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Introduction to Programming with Java: A Problem Solving Approach teaches the reader how to write programs using Java. It does so with a unique approach that combines fundamentals first with objects early. The book transitions smoothly through a carefully selected set of procedural programming fundamentals to object-oriented fundamentals. During this early transition and beyond, the book emphasizes problem solving. For example, Chapter 2 is devoted to algorithm development, Chapter 8 is devoted to program design, and problem-solving sections appear throughout the book. The third edition introduces several new Java language features, most of the end-of-chapter GUI sections and the final GUI chapters use JavaFX, and almost all end-of-chapter exercises are new.

Chapter 1. Introduction to Computers and Programming

Chapter 2. algorithms and design, chapter 3. java basics, chapter 4. control statements, chapter 5. using prebuilt methods, chapter 6. object-oriented programming, chapter 7. object-oriented programming-additional details, chapter 8. software engineering, chapter 9. arrays, chapter 10. arrays lists and an introduction to the java collections framework, chapter 11. recursion, chapter 12. type details and alternative coding mechanisms, chapter 13. aggregation, composition, and inheritance, chapter 14. inheritance and polymorphism, chapter 15. exception handling, chapter 16. files, buffers, channels, and paths, chapter 17. gui programming basics, chapter 18. gui programming-layout panes, additional gui components, chapter 19. gui programming-additional gui components, additional listeners, animation, appendix 1. ascii character set, appendix 2. operator precedence, appendix 3. java reserved words, appendix 4. packages and modules, appendix 5. java coding-style conventions, appendix 6. javadoc with tags, appendix 7. uml diagrams, appendix 8. number systems and conversions between them, about the author.

John Dean is an Associate Professor in the Computer Science and Mathematics Department at Park University. He earned a Ph.D. degree in computer science from Nova Southeastern University and an M.S. degree in computer science from the University of Kansas. He is Java certified and has worked in industry as a software engineer and project manager, specializing in Java and various Web technologies— JavaScript, JavaServer Pages, and servlets. He has taught a full range of computer science courses, including Java programming and Java-based web programming. He has authored a web programming textbook with a focus on client-side technologies HTML5, CSS, and JavaScript.

Raymond Dean is a Professor Emeritus, Electrical Engineering and Computer Science, University of Kansas. He earned an M.S. degree from MIT and a Ph.D. degree from Princeton University, and he is a senior member of IEEE. He has published numerous scientific papers and has 21 U.S. patents. He has industry experience in manufacturing HVAC equipment and energy-management controls, as well as in system energy analysis. At the University of Kansas, he taught a wide range of courses in electrical engineering and computer science.

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PW Skills | Blog

75 Basic Programming Problems and Tutorials for Practice

Solving Basic Programming Problems is the key to achieve success in coding challenges. Students must practice these basic programming problems !

Basic Programming Problems: Engaging in code challenges offers many benefits, serving as a dynamic tool to enhance problem-solving proficiency, deepen your comprehension of the programming language you work with, and acquaint yourself with diverse algorithms. If you aspire to elevate your programming skills, immersing yourself in coding is the most effective avenue.

The beauty of basic programming problems lies in their convenience—they provide a platform to hone your abilities through bite-sized problems, often eliminating the need to construct entire applications. This characteristic allows you to conquer these challenges swiftly, fostering a sense of accomplishment.

Moreover, code challenges are integral components of many coding interviews.

While your resume may showcase your skills and ability to articulate programming concepts, employers want to validate your practical coding capabilities. Tackling coding challenges during interviews becomes a testament to your proficiency and showcases your competence for the role.

Therefore, incorporating coding challenges into your routine sharpens your skills and is an invaluable preparation strategy for job interviews. To kickstart your coding journey, we have curated a collection of popular basic programming problems to pave the way for your continued growth.

Table of Contents

Basic Programming Problems Overview

Basic programming problems provide an essential foundation for individuals learning to code, offering a practical and hands-on approach to mastering fundamental concepts in programming.

problem solving approach in programming

These problems are designed to introduce beginners to the core coding principles, gradually building their problem-solving skills and comprehension of programming logic.

Whether you are a novice looking to embark on your coding journey or an experienced programmer aiming to reinforce your foundational knowledge, engaging with basic programming problems is a valuable practice.

These problems typically cover essential topics such as data types, loops, conditionals, functions, and basic algorithms, providing a well-rounded introduction to the key building blocks of programming.

The significance of basic programming problems extends beyond mere skill development; it serves as a stepping stone for individuals aspiring to pursue more advanced coding challenges and projects.

By grappling with these foundational problems, learners can cultivate a solid understanding of programming fundamentals, laying the groundwork for future exploration and mastery of more complex coding concepts. Basic programming problems are the cornerstone of a programmer’s educational journey, fostering a strong and resilient coding skill set.

Basic Programming Problems for Beginners

Starting your career in the programming field is  exciting and challenging. For beginners, mastering the basics is crucial, and what better way to do so than by solving basic programming problems ?

Basic Programming Problems Java

Here are some of the basic programming problems JAVA :

1) Hello World:

public class HelloWorld {

    public static void main(String[] args) {

        System.out.println(“Hello, World!”);

2) The sum of Two Numbers:

Add two numbers and print the result.

public class Sum {

        int num1 = 5, num2 = 10, sum;

        sum = num1 + num2;

        System.out.println(“Sum: ” + sum);

3) Factorial of a Number:

Calculate the factorial of a number.

public class Factorial {

        int num = 5;

        long factorial = 1;

        for (int i = 1; i <= num; ++i) {

            factorial *= i;

        System.out.println(“Factorial: ” + factorial);

4) Check Even or Odd:

Determine if a number is even or odd.

public class EvenOdd {

        int num = 8;

        if (num % 2 == 0) {

            System.out.println(num + ” is even.”);

        } else {

            System.out.println(num + ” is odd.”);

5) Reverse a String:

Reverse the characters in a given string.

public class ReverseString {

        String str = “Hello”;

        StringBuilder reversed = new StringBuilder(str).reverse();

        System.out.println(“Reversed String: ” + reversed);

Here are some theory-based basic programming problems Java:

1) Differences Between C++ and Java

  • C++: Not platform-independent, follows “write once, compile anywhere.”
  • Java: Platform-independent byte code allows programs to run on any machine.

Languages Compatibility:

  • C++: Compatible with most high-level languages.
  • Java: Incompatible with most languages, comparable to C and C++.

Interaction with the Library:

  • C++: Direct access to native system libraries, suitable for system-level programming.
  • Java: Requires Java Native Interface or library access, not direct call support.

Characteristics:

  • C++: Combines features of procedural and object-oriented languages.
  • Java: Known for automatic garbage collection, lacks support for destructors.

Semantics of the Type:

  • C++: Consistent semantics for primitive and object types.
  • Java: Inconsistent semantics between primitive and object types and classes.

Compiler and Interpreter:

  • Java: Compiled and interpreted language, source code compiles into platform-independent bytecode.
  • C++: Purely compiled language, source program compiles into object code, further executed.

2) Features of the Java Programming Language:

  • Easy: Java is considered easy to learn, with fundamental Object-Oriented Programming (OOP) concepts.
  • Secured Feature: Java provides a secured feature, ensuring the development of virus-free and tamper-free systems.
  • OOP: Java follows Object-Oriented Programming, treating everything as an object.
  • Independent Platform: Java compiles into platform-independent bytecode, interpreted by the Virtual Machine.

3) ClassLoader in Java:

  • A ClassLoader in Java is a subsystem of the Java Virtual Machine responsible for loading class files during program execution.
  • It is the first to load the executable file and includes Bootstrap, Extension, and Application classloaders.

4) Differences Between Heap and Stack Memory in Java:

  • Stack Memory: Allocated to each individual program. Fixed memory space.
  • Heap Memory: Not assigned to Java code initially but available during runtime. Used as needed by the Java code.

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Key Features:

  • Comprehensive Java Coverage: Delve into the intricacies of Core Java, unraveling the language’s syntax, features, and object-oriented programming concepts. From basic constructs to advanced topics, this course ensures a thorough understanding of Java.
  • Powerful Problem-Solving with DSA: Unlock the potential of Data Structures and Algorithms to efficiently solve complex problems. Acquire the essential tools and strategies to approach real-world challenges with confidence and precision.
  • Hands-On Learning: Immerse yourself in practical, hands-on exercises that reinforce theoretical concepts. Through coding exercises and projects, you’ll apply your knowledge, fostering a deeper understanding of both Java and DSA.
  • Expert Guidance: Benefit from expert guidance provided by seasoned instructors with extensive industry experience. Learn industry best practices and gain insights into the practical applications of Java and DSA.

Who Should Enroll:

  • Programming Enthusiasts
  • Students Pursuing Computer Science or Related Fields
  • Professionals Seeking to Strengthen Core Java and DSA Skills

Upon completion of “ Decode Java+DSA 1.0 ,” by PW you’ll emerge as a proficient programmer equipped with the skills to tackle diverse programming challenges. Whether you’re aiming to kickstart your programming career, enhance your academic pursuits, or upskill for professional growth, this course is your gateway to mastering Java and DSA. Elevate your programming prowess and embark on a journey of continuous learning and innovation.

Basic Programming Problems in C

The table below shows the basic programming problems in C :

Put your learning into action with hands-on projects that simulate real-world scenarios with Decode Full Stack Web Dev 1.0 by PW . From designing responsive user interfaces to implementing robust server-side functionalities, you’ll gain practical experience that enhances your proficiency.

Learn essential tools like Git for version control, ensuring collaborative and efficient development. Explore deployment strategies to showcase your applications to the world, covering platforms like Heroku.

Who Should Enroll

  • Aspiring Web Developers 
  • Computer Science Students 
  • Professionals Transitioning to Web Development 
  • Entrepreneurs Looking to Build Web Applications

Basic Programming Problems in Python

In addition to introducing you to Python’s syntax and structure, tackling basic programming problems in Python helps you improve your problem-solving skills. With tasks ranging from basic logic puzzles to intricate algorithmic difficulties, these issues offer an interactive method of learning Python and put you on the route to becoming a skilled programmer.

Basic Programming Problems in Javascript

Whether you aim to enhance your web development skills or explore the vast world of JavaScript applications, these problems cater to beginners, guiding them through the foundational aspects of programming in this versatile language. Below table showcases the basic programming problems in Javascript :

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Key Highlights

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Basic Programming Problems and Solutions

Here are 10 basic programming problems along with their solutions:

  • Hello World:

Problem: Write a program that prints “Hello, World!” to the console.

Solution (Python):

print(“Hello, World!”)

  • Sum of Two Numbers:

Problem: Write a program that inputs two numbers and prints their sum.

Solution (Java):

import java.util.Scanner;

public class SumOfTwoNumbers {

        Scanner scanner = new Scanner(System.in);

        System.out.print(“Enter first number: “);

        int num1 = scanner.nextInt();

        System.out.print(“Enter second number: “);

        int num2 = scanner.nextInt();

        int sum = num1 + num2;

  • Factorial of a Number:

Problem: Write a program to calculate the factorial of a given number.

Solution (C++):

#include <iostream>

using namespace std;

int factorial(int n) {

    if (n == 0 || n == 1)

        return 1;

        return n * factorial(n – 1);

int main() {

    int num;

    cout << “Enter a number: “;

    cin >> num;

    cout << “Factorial: ” << factorial(num) << endl;

    return 0;

  • Check Even or Odd:

Problem: Write a program that checks if a given number is even or odd.

Solution (JavaScript):

let number = 7;

if (number % 2 === 0) {

    console.log(number + ” is even”);

    console.log(number + ” is odd”);

  • Reverse a String:

Problem: Write a program to reverse a given string.

original_string = “Hello, World!”

reversed_string = original_string[::-1]

print(“Reversed String:”, reversed_string)

  • Fibonacci Series:

Problem: Generate the Fibonacci series up to a specific limit.

public class FibonacciSeries {

        int limit = 10;

        int firstTerm = 0, secondTerm = 1;

        System.out.println(“Fibonacci Series up to ” + limit + ” terms:”);

        for (int i = 1; i <= limit; ++i) {

            System.out.print(firstTerm + “, “);

            int nextTerm = firstTerm + secondTerm;

            firstTerm = secondTerm;

            secondTerm = nextTerm;

  • Check Prime Number:

Problem: Write a program to check if a given number is prime.

def is_prime(number):

    if number > 1:

        for i in range(2, int(number / 2) + 1):

            if (number % i) == 0:

                return False

        else:

            return True

        return False

if is_prime(num):

    print(num, “is a prime number.”)

    print(num, “is not a prime number.”)

  • Find Maximum Element:

Problem: Write a program to find the maximum element in an array.

int findMax(int arr[], int size) {

    int max = arr[0];

    for (int i = 1; i < size; ++i) {

        if (arr[i] > max) {

            max = arr[i];

    return max;

    int numbers[] = {5, 8, 2, 10, 3};

    int size = sizeof(numbers) / sizeof(numbers[0]);

    cout << “Maximum Element: ” << findMax(numbers, size) << endl;

  • Palindrome Check:

Problem: Write a program to check if a given string is a palindrome.

public class PalindromeCheck {

        String str = “level”;

        String reversedStr = new StringBuilder(str).reverse().toString();

        if (str.equals(reversedStr)) {

            System.out.println(str + ” is a palindrome.”);

            System.out.println(str + ” is not a palindrome.”);

  • Count Vowels and Consonants:

Problem: Write a program to count the number of vowels and consonants in a given string.

text = “Hello, World!”

vowels = “AEIOU

Benefits of Solving Basic Programming Problems

Solving basic programming problems offers numerous benefits for individuals looking to enhance their programming skills. Here are some key advantages:

Skill Development:

  • Coding Proficiency: Regular problem-solving helps improve your coding skills and fluency in programming languages.
  • Algorithmic Thinking: It fosters the development of algorithmic thinking, enabling you to devise efficient solutions to various problems.

Logical Thinking:

  • Problem Decomposition: Breaking down problems into smaller components and solving them enhances logical thinking and problem-solving abilities.
  • Pattern Recognition: Regular problem-solving helps in recognizing patterns and similarities between different problems, leading to more efficient solutions.

Learning New Concepts:

  • Exposure to Diverse Topics: Programming problems often cover a wide range of concepts, exposing you to different areas of computer science and software development.
  • New Algorithms and Data Structures: Exploring various problems introduces you to new algorithms and data structures, expanding your knowledge base.

Preparation for Interviews:

  • Technical Interviews: Many technical interviews for programming roles involve solving algorithmic and coding problems. Regular practice prepares you for such interviews and boosts your confidence.
  • Coding Challenges: Familiarity with common coding challenges often encountered in interviews is an asset.

Building a Portfolio:

  • Showcasing Skills: Solving problems allows you to build a portfolio of solutions that you can showcase to potential employers or on coding platforms.
  • GitHub Contributions: Uploading your solutions to platforms like GitHub demonstrates your coding proficiency and problem-solving ability.

Enhanced Efficiency:

  • Code Optimization: Regular practice encourages optimization, leading to more efficient and cleaner code.
  • Time Complexity Awareness: Problem-solving helps in understanding and considering time complexity, contributing to the creation of scalable solutions.

Community Engagement:

  • Online Communities: Engaging in online coding communities allows you to discuss problems, learn from others, and gain insights into alternative solutions.
  • Peer Learning: Collaborating with peers on coding challenges can provide different perspectives and foster a collaborative learning environment.

Career Advancement:

  • Competitive Edge: Building strong problem-solving skills sets you apart in a competitive job market, enhancing your employability.
  • Adaptability: A wide range of problem-solving experiences makes you more adaptable to different tasks and projects.

Personal Satisfaction:

  • Sense of Achievement: Successfully solving programming problems brings a sense of accomplishment, boosting confidence and motivation.
  • Continuous Learning: It fosters a mindset of continuous learning, crucial in a rapidly evolving field like programming.

In summary, regular practice of solving basic programming problems contributes significantly to skill development, logical thinking, and overall proficiency in the field of programming.

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Machine Learning vs Traditional Programming: Choosing the Right Approach for Your Projects

Machine Learning vs Traditional Prog.

Machine learning vs traditional programming comparisons help professionals pick an approach to solving problems with computer systems. While traditional programming has been a mainstream tool for quite some time, machine learning (ML) tools coupled with AI are the new favorite solution for businesses, consumers, and developers. 

A certain level of computer intelligence comes into play with machine learning models since algorithms can learn from their environment and input data to improve with time. On the other hand, traditional programming systems depend entirely on user input to determine the solution’s output. With the growing debate on which approach is better for problem-solving and for beginners to learn, we have compiled a detailed comparison of machine learning vs traditional programming in this guide.

In the following sections, we consider the differences between both approaches and the types of solutions that can be built with each of them.

What is the main difference between machine learning vs traditional programming?

achine learning vs traditional programming applicationsional-programming

The main difference between machine learning and traditional programming is that while the latter depends entirely on user input, the former can utilize algorithms that enable it to learn from its environment and experience, allowing the computer to act independently to some capacity.

In this sense, traditional programming would have users write specific instructions to solve problems. They are designed in a manner where their developer has to create the boundaries, rules, and logic that tie each program process to a specific outcome. This approach has defined traditional computers and is still widely used for executing basic operations like calculations or sorting data.

On the other hand, machine learning systems are defined more by the training data provided to them than by written rules. This helps the algorithm prepare for different scenarios as it can make classifications and predictions based on new data. Some typical applications of machine learning algorithms in modern programs are generative AI, natural language processing, image generation, and fraud detection.

Let’s take a detailed look into some of the common differences between these two approaches:

Data differences

The first difference between these two approaches is based on the kind of data they use for their processes. Machine learning can work through various dynamic and unstructured datasets to identify patterns and perform predictive analysis. On the other hand, traditional programming is entirely dependent on structured and static data.

One example of this can be considered with a program designed to predict stock prices. The machine learning algorithms can view everything from social media sentiment and official company data to news articles for understanding market patterns and predicting future trends that could impact stock prices. On the other hand, traditional programming would attempt to predict stock prices by looking at fixed data sets for financial information, like past prices.

Flexibility differences

There are also apparent differences in flexibility with both approaches, which affect the scale and range of problems they can handle. Machine learning also has the upper hand in this comparison as it can learn from different data sources and adapt quickly. As for traditional programming, solutions are much less flexible and generally rigid in comparison.

For instance, if we design a traditional program to perform a specific task like solving sums, we cannot easily change it to sort through graphic data. However, changing these criteria with a machine-learning model is much easier. For instance, one model designed to classify images of animals can easily be retrained to identify other items like houses or vehicles.

Application differences

As for the solutions and applications built with both approaches, there are also apparent differences where machine learning is mainly used for dynamic and complex tasks that require in-depth analysis and continuous adaptation and learning. On the other hand, traditional programming systems are best used for tasks that are repeatable and well-defined in basic logic.

One example of traditional programming in this sense is that it is used for tasks like building out a database or creating a website. In contrast, machine learning is used for tasks like natural language processing or predicting customer behaviors.

Expertise differences

Lastly, there are differences in the expertise required to build solutions with machine learning vs traditional programming. For traditional approaches, developers must be well-versed in coding languages, software engineering frameworks, and principles. On the other hand, machine learning experts must be comfortable working with complex algorithms, neural networks, deep learning, statistical models, and data science.

Some languages machine learning engineers use are R and Python, while traditional programmers usually build solutions using Java or C++.

What is machine learning best for?

machine learning applications

Machine learning is ideal for tasks that require complex algorithms, statistical models, artificial intelligence, decision-making, and pattern recognition. These algorithms can quickly work through large amounts of data to detect and present insights and patterns that would be impossible to conceive with traditional programming techniques. They also allow AI solutions to improve performance over time through experience and continuous learning.

To learn more about how to become a machine learning engineer, check out our detailed guide on the topic!

Some typical machine learning applications are predictive maintenance, fraud detection, natural language processing, and recommendation systems. Let’s take a more detailed look at each of these.

Applications of machine learning solutions

Recommendation systems.

One of the most common uses of machine learning (ML) models is for building recommendation systems that can study user profiles, preferences, and behavioral patterns to give product or service suggestions to the end user.

Restaurants often use this to suggest meal choices to customers depending on what they ordered online previously. Entertainment providers like Netflix can also use these systems to recommend new TV shows and movies to different users based on their past choices within the app.

Natural language processing

Ml algorithms are often used to optimize natural language process (nlps) systems that are a part of software solutions intended to understand human language and input. some common examples of nlp solutions are chatbots and voice assistants that use training data to understand user queries and provide appropriate responses., image recognition.

machine learning image recognition

With its power of pattern recognition, prediction, and automation, machine learning is a powerful substitute for human resources to some degree, making it a staple in every modern industry.

What is traditional programming best for?

traditional programming solutions

Traditional programming has been the more common approach for problem-solving with computers. It is particularly excellent for tasks that rely on rule-based language, precise input-output scenarios, and little to no variation. This makes them perfect for building basic sites and creating software solutions with fixed criteria or databases. Besides that, this approach is ideal for tasks based on mathematical modeling. Check out our detailed guide to learn more about the differences between software engineering and web development!

The solution for most problems the traditional programming approach solves is clearly defined at the start of the process, like with most software development projects. Here is a look at some of the more common applications of this problem-solving approach:

Applications of traditional programming

Rule-based systems.

It is possible to use traditional programming to create systems that run by specific rules and have set criteria for decision-making that are usually pre-defined. One example is the spam filter that can be built into email systems with traditional programming. The filter will look for specific words in all incoming emails and mark any that match the fixed criteria as spam.

Real-time systems

Additionally, traditional programming languages can be used for real-time systems where the computer can react quickly to predictable inputs that lead to a pre-defined solution. For example, this use is often witnessed with traffic light systems that use traditional programming to determine when the system changes to light, depending on factors like traffic flow.

Simple calculations

Traditional programming is excellent for simple calculations or creating systems that run on deterministic behavior, like a banking app that can easily calculate interest based on the amount and duration of the deposited sum.

Other basic calculations like multiplication, division, addition, and subtraction can be tackled by creating a basic calculator app. But, overall, the traditional programming approach works best where the logic of the problem and solution is well-defined and straightforward, and the input/output system is deterministic.

While machine learning and traditional programming are similar in that they are used to solve problems and improve the user experience with computers, the approaches are entirely different in executing their purpose. Traditional programming is a part of several applications, but it is impossible to automate decision-making processes efficiently using tools with a traditional focus.

Despite the risks associated with the exponential growth of artificial intelligence and machine learning, the latter is ideal for handling dynamic and complex data, including big datasets. The ultimate decision for your chosen approach will depend on your resources, the problem you are solving, and the application you are building. If you’re interested in data science and machine learning and have considered moving into the industry, book a free career consultation with one of our career experts today and get the guidance you need to reach your goal. 

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IMAGES

  1. Six Steps to Solving a Programming Problem Infographic

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  2. 6 Ways to Improve Your Programming Problem Solving

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  4. Problem Solving In Programming

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  1. Vid03 Problem formulation and Integer Linear Programming

  2. Do this for the next 100 days to Improve your Problem Solving Skills 🔥

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  6. What is an algorithm?

COMMENTS

  1. How to Solve Coding Problems with a Simple Four Step Method

    Step 1: Understand the problem. When given a coding problem in an interview, it's tempting to rush into coding. This is hard to avoid, especially if you have a time limit. However, try to resist this urge. Make sure you actually understand the problem before you get started with solving it. Read through the problem.

  2. What is Problem Solving? An Introduction

    Problem solving, in the simplest terms, is the process of identifying a problem, analyzing it, and finding the most effective solution to overcome it. For software engineers, this process is deeply embedded in their daily workflow.

  3. How To Approach A Coding Problem

    These steps you need to follow while solving a problem: - Understand the question, read it 2-3 times. - Take an estimate of the required complexity. - find, edge cases based on the constraints. - find a brute-force solution. ensure it will pass. - Optimize code, ensure, and repeat this step.

  4. How to think like a programmer

    Try a solution. If that doesn't work, try another one. If that doesn't work, repeat step 2 until you luck out. Look, sometimes you luck out. But that is the worst way to solve problems! And it's a huge, huge waste of time. The best way involves a) having a framework and b) practicing it.

  5. How to Develop Problem Solving Skills in Programming

    Problem Solving is an essential skill that helps to solve problems in programming. There are specific steps to be carried out to solve problems in computer programming, and the success depends on how correctly and precisely we define a problem. This involves designing, identifying and implementing problems using certain steps to develop a computer.

  6. A Guide to Problem-Solving for Software Developers with Examples

    It's even better if the members of the team are used to swim in uncertainty, and take it as a challenge more than a chore. The process described above is just an example; in practice it's often more chaotic. For example, even when a decision is made, your brain might still continue to process the problem passively.

  7. 10 Steps to Solving a Programming Problem

    1. Read the problem at least three times (or however many makes you feel comfortable) You can't solve a problem you don't understand. There is a difference between the problem and the problem you think you are solving.

  8. What exactly is a programming paradigm?

    While developing an application or solving a simple problem, we often say "If I had more time, I would have written a simpler program". The reason is, we made a program with greater complexity. The less complexity we have, the easier it is to debug and understand. The more complex a program becomes, the harder it is to work on it.

  9. Problem-Solving Strategies for Software Engineers

    Write out the problem. Your problem won't always come right out and say: "It's me, hi. I'm the problem, it's me.". In fact, something that often gets in the way of solving a problem is that we zero in on the wrong problem. When pinpointing a problem, you can try borrowing a UX research technique that's part of the design thinking ...

  10. Problem Solving

    From his book, "Think Like a Programmer", V. Anton Spraul defines problem solving in programming as: ... Decomposing problems into smaller and easier to solve subproblems is a much better approach. Decomposition is the main way to deal with complexity, making problems easier and more approachable to solve and understand.

  11. CBSE Class 11

    1. Understanding the problem: To solve any problem it is very crucial to understand the problem first. What is the desired output of the code and how that output can be generated? The obvious and essential need to generate the output is an input. The input may be singular or it may be a set of inputs.

  12. Introduction of Programming Paradigms

    Programming paradigm is an approach to solve problem using some programming language or also we can say it is a method to solve a problem using tools and techniques that are available to us following some approach.

  13. The Beginner Programmer's guide to Problem Solving [With Example]

    Step 3: Connect the dots (Integration) You have solved individual problems. Now it is time to connect the dots by connecting the individual solution. Identify those steps which will make the solution or the program complete. Typically in programming, the dots are connected by passing data that is stored in variables.

  14. 7 Tips on How to Approach and Solve Coding Problems Successfully

    Create a flow chart illustrating the problem. Break it into subproblems or smaller sections. Solve each section by creating independent functions. Arrange the functions in the order you need, thereby merging them all for the final solution. In case of errors, you can debug the code and search for an alternate approach.

  15. Enhancing problem‐solving skills of novice programmers in an

    Novices should focus both on problem-solving skills and programming knowledge (syntax and semantics) in learning to program. ... On the other hand, the PAAM-based approach first emphasizes problem-solving strategies without much worry about syntax and semantics of the programming language. In this way, the novices get a better understanding of ...

  16. Python Programming : Using Problem Solving Approach

    The book begins with an introduction to computers, problem solving approaches, programming languages, object oriented programming, and Python programming. Separate chapters dealing with the important constructs of Python language such as control statements, functions, strings, files, data structures, classes and objects, inheritance, operator ...

  17. Introduction to Programming with Java: A Problem Solving Approach

    It does so with a unique approach that combines fundamentals first with objects early. The book transitions smoothly through a carefully selected set of procedural programming fundamentals to object-oriented fundamentals. During this early transition and beyond, the book emphasizes problem solving.

  18. Top-Down Approach vs. Bottom-Up Approach

    Contrary to the top-down approach, bottom-up programming focuses on designing an algorithm by beginning at the very basic level and building up as it goes. In this approach, the modules are designed individually and are then integrated together to form a complete algorithmic design. ... Problem-Solving: In complex problem-solving scenarios, ...

  19. 75 Basic Programming Problems and Tutorials for Practice

    Basic Programming Problems: Engaging in code challenges offers many benefits, serving as a dynamic tool to enhance problem-solving proficiency, deepen your comprehension of the programming language you work with, and acquaint yourself with diverse algorithms.

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    COUPON: RENT Introduction to Programming with Java: A Problem Solving Approach 3rd edition (9781259875762) and save up to 80% on 📚textbook rentals and 90% on 📙used textbooks. Get FREE 7-day instant eTextbook access! ... A Problem Solving Approach 3rd edition (978-1259875762) today, or search our site for other 📚textbooks by Dean.

  21. Machine Learning vs Traditional Programming: Choosing the Right

    The solution for most problems the traditional programming approach solves is clearly defined at the start of the process, like with most software development projects. Here is a look at some of the more common applications of this problem-solving approach: Applications of traditional programming Rule-based systems

  22. Structured Approach to Problem Solving

    By the end of this course, you will be able to: 1. Explain the different stages of a data science project 2. Discuss some of the tools and techniques used in data science. 3. Apply structured thinking to solving problems and avoid the common traps while doing so 4. Apply human-centric design in problem-solving.

  23. ISE Introduction to Programming with Java: A Problem Solving Approach

    Introduction to Programming with A Problem Solving Approach teaches the reader how to write programs using Java. It does so with a unique approach that combines fundamentals first with objects early. The book transitions smoothly through a carefully selected set of procedural programming fundamentals to object-oriented fundamentals. During this ...

  24. PDF Mathematical Problem-solving by Means of Computational Thinking and

    and programming to support problem solving in mathematics education research during the last few years. ... Our hypothesis is that developing and using pedagogical approaches for mathematical problem-solving by means of CT and programming may open new opportunities. At the same time, new challenges may become visible, and each of these ...

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