How can you improve your code's performance, readability, and reusability?

As a software developer, I've learned the vital role of improving code performance, readability, and reusability. Utilizing appropriate data structures and algorithms has been central to achieving these objectives. I carefully selected data structures for efficiency and favored low-complexity algorithms, ensuring faster execution. Emphasizing readability, I used clear names and comments, fostering collaboration. For reusability, I created modular components. Comprehensive testing ensured correctness and robustness. Code reviews provided valuable feedback. Continuous learning kept me updated. Embracing these principles enriched my journey, delivering efficient, maintainable, and reusable solutions for developers and users alike.

As a software developer, I've learned the vital role of improving code performance, readability, and reusability. Utilizing appropriate data structures and algorithms has been central to achieving these objectives. I carefully selected data structures for efficiency and favored low-complexity algorithms, ensuring faster execution. Emphasizing readability, I used clear names and comments, fostering collaboration. For reusability, I created modular components. Comprehensive testing ensured correctness and robustness. Code reviews provided valuable feedback. Continuous learning kept me updated. Embracing these principles enriched my journey, delivering efficient, maintainable, and reusable solutions for developers and users alike.

As a software developer, I've learned the vital role of improving code performance, readability, and reusability. Utilizing appropriate data structures and algorithms has been central to achieving these objectives. I carefully selected data structures for efficiency and favored low-complexity algorithms, ensuring faster execution. Emphasizing readability, I used clear names and comments, fostering collaboration. For reusability, I created modular components. Comprehensive testing ensured correctness and robustness. Code reviews provided valuable feedback. Continuous learning kept me updated. Embracing these principles enriched my journey, delivering efficient, maintainable, and reusable solutions for developers and users alike.

As a Software Developer, optimizing your code is important not only to make your code run faster but also, make your code more readable and easier to maintain and the approach you take, depends on the language you are going to use, the platform you are targeting, your specific goals and context, along with appropriate data structures and unbias efficient algorithms. Other things, I can think , is data caching techniques used and avoiding unnecessary I/O.

Performance: To improve code performance, you can use techniques such as caching, lazy loading, and optimizing database queries. Readability: Writing readable code is important for making your codebase maintainable and easy to understand. Reusability: Code reusability is important for reducing development time and improving code quality. Testing: Testing is an important part of software development that can help you catch bugs and ensure that your code is working as expected. Refactoring: Refactoring is the process of improving existing code without changing its external behavior. Code reviews: Code reviews are a great way to get feedback on your code and improve its quality.

I have found that adopting standards that promote simplicity and ease of reading increases overall velocity of the team. We've all probably sat back and looked at a fine piece of complex code with satisfaction, only to revisit it a month or more later and realize we can't remember what was happening. Asking for good in-line and PR comments can make a huge difference in a teams velocity. If a good commenting standard is adopted then a quick read often unlocks things and negates the need to ask the original author. Further, it can help convey future intent based on ways to improve the existing code.

Writing modular and cohesive code also implies writing simple code. We should not go over-board with design principles and patterns just for a simple use-case. We can start writing very simple code in terms of small functions and small classes. With more usage, we can refactor the same to apply design patterns and principles as applicable.

Encapsulation essentially is the wrapping up of related functionalities and properties of a component or a unit. Abstraction is the hiding of implementation details and exposing only the interface.

When building larger applications, modular code is the way to go. It's easier to maintain a code base that's modular than one that is large and overly complex. Modular code makes it easier to test and debug. Additionally, modular code is easier for new developers to learn and understand, as they will be able to get up to speed quicker with individual components and modules than with a large codebase.

When sepaating the task into subtasks let's try to separate the tasks, which are more common and develop code for them which can be used not only in the current project, but in other too. This way we build our toolbox and become more productive in the long run. Examples: fill the options in a combo box or list box by a SQL statement, cut a string to max length without cutting words in half, etc.

The code should always be self-explanatory. If you can't easily understand what you're reading, refactor and improve it so that the next one can spend less time understanding it. Don't forget the Uncle Bob's boy scout rule : "Always leave the code you're editing a little better than you found it"

Something not to forget: the refactored code must work. I'm sure many good developers experienced troubles after doing "juts a small change". So, before thinking of refactoring better organize regression testing (manually or, better, automatically) and run all teste after every change.

Refactoring is a necessary part of the development lifecycle - one that enables you to make small changes to improve the design and maintainability of your codebase without breaking functionality. Over time features are added, developers learn and employ new practices, and code naturally becomes more brittle as it deviates from its original purpose. After all - it's easy to insert a few lines of logic to get something done, but over time that makes once beautiful code a mess. If you can't quickly look at a piece of code and understand what it's doing, if there is duplication or other code smells - take the time to refactor your code base. Your future self will thank you due to better maintainability and ease of changes in the future.

Use proper self understandable variable names and function names and include proper comments. Follow different principles like dry, yagni and solid principles while writing code.

Code should speak what it does, not the documentation. I prefer the DDD approach which enforces us to write better quality code and manageable code.

Add observability to gain performance insights Adding Open Telemetry (OTel) instrumentation to code can greatly improve its performance by providing valuable insights into how the code is actually running in production. With instrumentation, developers can easily identify bottlenecks and performance issues, as well as track important metrics like latency, error rates, and throughput. With traces and logs those bottlenecks can be further investigated and understood. By identifying areas of code that are slow or causing errors, developers can take targeted actions to optimize and improve the overall performance of the application. OTel is an open standard and therefore does not create a lock-in into a specific tool.

The code must read like a book. Add comments only in special cases where the processing is strange, extraordinary orthe algorithm is difficult. Half an hour spent to find the best word for a variable or functiona name pays off many times in the long run.

Also to add, if you can when sorting or searching data structures, try to do so "in-place" so that you spend less time and resources duplicating large structures on memory and thus needing to wipe them again. This scales with the size of what you are operating on. Also, if you can when fetching data from any source such as an API or disk, do so in parallel (non-blocking asynchronous) as to allow other parts of the program to load and run as well, unless continued execution is entirely dependent on that data of course, but otherwise aim not to stop or slow down independent processes due to others.

To add to some of the other great insights, I would add that it is important to consider the complete stack or ecosystem when creating solutions. My primary environment is .Net, so taking into consideration how the framework, the database and the code all relate can add optimization to your solution. In many cases the .Net framework can handle use cases when leveraged in a more efficient way than a “roll your own” method. In other cases, perhaps that is not the case. But it is always worth adding a pause in the haste to write the code, so as to consider the options you have. Pause, consider, then code.