Application developers face common logical reasoning challenges. Can you identify and overcome them?

To avoid making these mistakes, developers need to be careful. They should use clear and precise language, question their assumptions, and double-check where they're getting their information from. It's also helpful to get feedback from other people to make sure you're not overlooking anything.

Understanding the problem: Before you can write code to solve a problem, you need to fully understand the problem itself. This includes understanding the requirements, the constraints, and the expected outcome. If the problem is complex, try breaking it down into smaller, more manageable parts. Choosing the right data structures and algorithms: The choice of data structures and algorithms can greatly affect the efficiency and readability of your code. Debugging: When your code doesn't work as expected, you need to figure out why. This often involves logical reasoning to trace the execution of your code and understand where it deviates from what you expected. Writing test codes and organising your code

Application developers often encounter logical reasoning challenges in their work. 1. **Solving complex problems:** Developers must often break down complex problems into smaller, manageable tasks. 2. **Debugging code:** When code isn't working as expected, developers need to use logical reasoning to identify and fix the issue. 3. **Designing software architecture:** Developers must design software that is modular, scalable, and maintainable. 4. **Understanding algorithms and data structures:** To write efficient code, developers need to understand how different algorithms and data structures work. 5. **Learning new technologies:** Developers must constantly learn new technologies and programming languages.

Assumptions are frequently a problem in development environments. This is why thinking only as a developer can be detrimental to work. To avoid biases and assumptions, we need to exercise our mind to perform a dry run of the requirements and see if everything checks out and immediately seek feedback whether it seems like we are taking the right direction or not. Not always this is possible, but a clear and precise language goes a long way especially when we are communicating in a verbal way without any code support. Only after that and getting an acknowledgement from product and DEVs should we start thinking about code.

Edge cases, or corner cases can appear out of the blue and sometimes they appear where we don't want them to appear and that is in production. To minimize these situations, we need put some more effort into thinking further than the requirements presented and constantly contributing to a feedback loop between the whole team involved. It is not always possible to get the idea across about some corner cases and they can frequently be ignored, but doing that will at least prepare you to take action if that edge case appears in production and you find yourself in the need to make a hot-fix.

To master data structures and algorithms, move beyond mere memorization and cramming about time and space complexity etc. Instead, focus on their practical applications in solving real-world problems. When searching for specific data, explore different search algorithms. Understand how they work. For hierarchical information, consider trees—they represent parent-child relationships and file systems. Optimize access efficiency using stacks (for last-in-first-out scenarios) or queues (for first-in-first-out situations). Actively practice on platforms like Codewars and leetcode as they test your problem solving abilities. Start with fundamental problems. As you implement these concepts write some pseudocode to enhance your understanding.

Algorithms and data structures are some of the most important concepts of data science and computer science and for a good reason. They carry data around. Time and space complexity are also very important and at University we may be led to think that they are only theoretical concepts that we just learn there and don't use them again. But these concepts, if understood in an instinctive way, do help a lot in real world projects.

Rubber ducking sounds quite bizarre when you think of it but it does help. When we are debugging, thinking out loud can really shine a light on ideas that we may have not thought before. But before that, we need to get into the problem and find out where it is being created to be able to start thinking about it. A good strategy on how to debug the code we are investigating can save a lot of time in finding out bugs and solving them.

Although many will judge design patters and the whole philosophy behind SOLID, we cannot escape the fact that these have become a part in our software development world. A good understanding S.O.L.I.D. is very important and especially if we are working with frameworks like Micronaut or the Spring Framework, it forms an intrinsic part of that development. Even with languages like Kotlin, the 5 principles of SOLID are there. And MVC is the whole backbone of the Spring Framework even when thinking about a reactive application. We have all created objects that have one responsibility, created objects where we can create sub classes, but not modify some inherited methods or properties, have created interfaces and we've used dependency inversion.