Tips for Writing Readable Code

5 lessons I learned from reviewing code in 125+ interviews I've reviewed over 125 code snippets as an interviewer at Google and Amazon and trust me, clean code in an interview can be the difference between a "lean hire" and a "strong hire." Here are 5 tips to instantly improve your interview code: 1️⃣ Meaningful names: - Ditch x, y, and z. - Use names that describe the variable's purpose. - Example: item_count instead of x. - Makes your code so much easier to follow. - Imagine debugging code with variables like "value1" and "temp2" - a nightmare for both you and the interviewer! 2️⃣ Modular functions: - Break down large functions into smaller ones. - Each function should do one thing. - Instead of one giant calculate_discount() function. - Separate get_base_price(), add_coupon(), and calculate_final_price(). - This shows you can write organized and reusable code. 3️⃣ Pass by Reference: - When appropriate, pass variables by reference. - Avoids unnecessary copying. - Often makes your code faster. 4️⃣ Clean formatting: - Consistent indentation and spacing matter. - Make formatting while coding a habit. - Simple rule: Go to the next line after cleaning up the current one. - The interviewer would be more willing to help debug your code if they can see that it is length * (*width.begin()) and not length**width.begin(). 5️⃣ Readability is King: - Imagine the interviewer reading your code. - Is it easy to follow? - If not, refactor it. - Get rid of that nested ternary (?). - Instead use an if-else block for clarity. - I know being a one-line-coding-ninja feels great. - But it is wiser to not do that in our interviews. Start making these small changes. And trust me, you'll see a big difference - Your interviewers will love to help and work with your code more. 🌻 Let's make this post even more useful by sharing your top clean code tips! #cleancode #coding #interview #softwareengineering #tech #google #amazon

🧩 Why I stopped writing clever code. I started coding as a competitive programmer in high school. We were given five hours to solve five to ten hard algorithmic problems. Speed from thought to code matters. Code brevity matters. Even my typing speed matters. When I started working in a real company, I took a secret pride in writing short and clever codes, especially those magical one-liners. It made me feel quick and smart. I thought that it was a mark of expertise. But after the 17th "What does this line do?" DM, I felt that the problem was not my teammate's intelligence but rather my own code. If only me can understand and maintain the code, I haven't really built a system, I've made a puzzle 😂 🧑💻 I learned that readable code is about future-proofing my work: - The best code is written for humans first, computers second. - I'll thank myself 6 months later when I come back to fix a bug at 2am. - My new teammates will onboard faster. - Bugs are also caught earlier because the review is easier. ⁉️ How to make your code more readable? - Use clear, descriptive variable and function names. No more x, foo, or doStuff. - Write comments, but not essays. Explain the “why,” not the obvious “what.” - Break down complex logic into smaller, well-named functions. - Stick to consistent formatting and style. Clever one-liners may win you programming contests, but readable code wins you trust from your team 😉

A hill I'm absolutely willing to die on: LLM-generated code is ruining the art of code commenting, and it's creating a maintenance nightmare. After working closely with AI coding assistants for the past couple of years, I noticed the same three patterns over and over: - No comments at all. - Comments that read like a git diff, explaining only what changed in the last prompt. - A massive 30-line function comment above a 20-line function, while the actual logic in body remains completely uncommented. This drives me insane. When you read a technical document, you don't want to see the author's revision history. You want the final design presented clearly. Code is no different. Code is a document. It's meant to be read, understood, and maintained by humans. I know the "self-documenting code" crowd will disagree, but good code and good comments aren't mutually exclusive. Clear naming explains what the code is doing. Good comments explain why it's doing it, describe assumptions, and provide context that isn't obvious from the implementation. If you're using LLMs to write software, you have to teach them what readable code looks like AND make sure they follow those principles. This is what I use: - Distribute the context: Replace giant comment blocks with concise, inline comments that explain logical sections of code. Let the reader absorb context as they read. - Respect the human eye (old IBM standard): Most methods should fit on a single screen. If someone has to scroll through an entire function just to understand it, the abstraction probably needs work. - Write code like a narrative: Start with the main entry point, then place helper functions in the order they're called. Let the file read from top to bottom like a story. LLMs can generate syntax very fast, but maintainable software still requires human craftsmanship. Don't let AI turn your codebase into a black box of giant functions and giant comment blocks. Write code like a document.

You inherit someone else’s bioinformatics code. No comments. No structure. Variable names like x1, foo, temp2. And now it’s your problem. Let’s talk about that experience—and how to do better. 1/ Ever opened someone’s script and felt instant regret? No README No comments Hard-coded paths Copy-pasted blocks No functions You’re not alone. Code without structure is like a freezer full of unlabelled tubes. Sure, it runs. But good luck figuring out what anything does. 3/ Bad practices hurt you the most. Even if you wrote the code. You: “I’ll remember this later.” Also you (6 weeks later): “Who wrote this garbage?” Still you. 4/ On the flip side: Well-structured code feels like a gift. Functions are defined. Comments explain the logic. Each section is modular. You can re-use it. You can trust it. 5/ Here’s what I’ve learned from writing and inheriting messy code: Bad code punishes future you. Good code rewards collaborators. 6/ What are some good coding practices in bioinformatics? Use clear variable names Comment your logic, not just what each line does Break repetitive steps into functions Keep a README and usage example Use relative paths and config files 7/ Avoid this: x1 <- read.table("data.txt") temp2 <- x1[which(x1[,3] > 10), ] Prefer this: expression_data <- read.table("data.txt", header=TRUE) high_expr <- subset(expression_data, expression > 10) Make it obvious what’s happening. 8/ Turn repeated blocks into functions: filter_by_threshold <- function(data, column, threshold) { subset(data, data[[column]] > threshold) } Now your code is DRY (Don’t Repeat Yourself) and reusable. 9/ Keep outputs organized: mkdir -p results/qc mkdir -p results/plots If your outputs are sprinkled across your desktop, it’s time to rethink. 10/ Bonus: write a run_pipeline.sh that chains all your steps. Use snakemake or nextflow if it gets too big. Even a bash script with clear comments beats scattered commands. 11/ Want to learn how to write better code? Read other people’s good code. You’ll learn tricks no tutorial teaches. 12/ Good code is a form of respect. For yourself. For your collaborators. For the person who inherits your project next. Write like someone else has to read it. Because they will. 13/ Bioinformatics isn’t just about solving problems. It’s about communicating how you solved them. Your code is your paper in progress. Write it like you’re proud of it. I hope you've found this post helpful. Follow me for more. Subscribe to my FREE newsletter chatomics to learn bioinformatics https://lnkd.in/erw83Svn

If you're a software engineer doing these three things, you may be making it harder for your teammates with dyslexia. Hey, I'm Bree, a software engineer and developer advocate, and I'm dyslexic. While I don't speak for everyone with dyslexia, here are three common habits that often make collaboration more difficult than it needs to be: 1️⃣ Skipping comments because “the code is self-explanatory.” It might be to you, but clear comments help me process and retain logic faster. When crafted correctly, they provide clarity and context. 2️⃣ Saying “Just read the code.” Reading code is a great way to learn and establish good programming habits, but this assumes that everyone processes when they're reading the same way. Reading ≠ comprehension. I might need to jump between files or revisit the same block several times before it clicks. 3️⃣ Using variable names that are too similar. Think: handleInput, handleInputs, inputHandler, handlerInput My brain blurs these together. Descriptive, distinct naming helps reduce cognitive load. At the end of the day, clean code is great. But inclusive code is better. Just because something is clear to you doesn’t mean it’s universally understandable. Thoughtful repetition, comments, and accessible naming conventions aren’t "extra." Write code as if someone different from you is going to maintain it.

I’ve seen teams argue for hours about tabs vs spaces, but skip the basics that actually make code easier to read and maintain. Here’s what really moves the needle for Python projects: 1) Write code that explains itself. Clear names and small functions solve half the pain. 2) Treat PEP 8 as a baseline, not a religion. Consistency matters more than strictness. 3) Add type hints. They save time, catch silly mistakes, and make the code easier for teammates and tools to reason about. 4) Keep functions focused. If it’s hard to describe what it does in one line, it’s trying to do too much. 5) Handle errors thoughtfully. Catch what you expect and log what you need. 6) Document the “why,” not the obvious. 7) Clean imports, meaningful tests, and no random magic values sprinkled around. These simple habits make Python code kinder to whoever reads it next -including future you. #python #codingstandards #codequality #cleancode #bestpractices #programmingtips Follow Sneha Vijaykumar for more... 😊

Best Practices for Writing Clean and Maintainable Code One of the worst headaches is trying to understand and work with poorly written code, especially when the logic isn’t clear. Writing clean, maintainable, and testable code—and adhering to design patterns and principles—is a must in today’s fast-paced development environment. Here are a few strategies to help you achieve this: 1. Choose Meaningful Names: Opt for descriptive names for your variables, functions, and classes to make your code more intuitive and accessible. 2. Maintain Consistent Naming Conventions: Stick to a uniform naming style (camelCase, snake_case, etc.) across your project for consistency and clarity. 3. Embrace Modularity: Break down complex tasks into smaller, reusable modules or functions. This makes both debugging and testing more manageable. 4. Comment and Document Wisely: Even if your code is clear, thoughtful comments and documentation can provide helpful context, especially for new team members. 5. Simplicity Over Complexity: Keep your code straightforward to enhance readability and reduce the likelihood of bugs. 6. Leverage Version Control: Utilize tools like Git to manage changes, collaborate seamlessly, and maintain a history of your code. 7. Refactor Regularly: Continuously review and refine your code to remove redundancies and improve structure without altering functionality. 8. Follow SOLID Principles & Design Patterns: Applying SOLID principles and well-established design patterns ensures your code is scalable, adaptable, and easy to extend over time. 9. Test Your Code: Write unit and integration tests to ensure reliability and make future maintenance easier. Incorporating these tips into your development routine will lead to code that’s easier to understand, collaborate on, and improve. #CleanCode #SoftwareEngineering #CodingBestPractices #CodeQuality #DevTips

𝐇𝐨𝐰 𝐓𝐨 𝐖𝐫𝐢𝐭𝐞 𝐁𝐞𝐭𝐭𝐞𝐫 𝐚𝐧𝐝 𝐂𝐥𝐞𝐚𝐧𝐞𝐫 𝐂𝐨𝐝𝐞 Explore these tips to write better code 👇 "Any fool can write code that a computer can understand. Good programmers write code that humans can understand." — Martin Fowler. I have seen a lot of experienced developers writing code that is hard to read and not enjoyable to work with. I am sure you have worked with such code. I am passionate about writing code that is easy to read and maintain. Today I want to share some actional tips that will make your code much better: 𝟏. 𝐍𝐚𝐦𝐢𝐧𝐠 When naming your variables, methods, and classes, ensure you give them clear and meaningful names. Good naming conventions enhance code readability. 𝟐. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐑𝐞𝐝𝐮𝐧𝐝𝐚𝐧𝐭 𝐂𝐨𝐦𝐦𝐞𝐧𝐭𝐬 𝐢𝐧 𝐓𝐡𝐞 𝐂𝐨𝐝𝐞 Often poor naming leads to comments in the code explaining the intent. This is a bad practice. Such comments clutter your code, make it less readable and can become outdated. Your code is your source of truth. Comments should explain the WHY, not the WHAT. 𝟑. 𝐅𝐨𝐫𝐦𝐚𝐭 𝐂𝐨𝐝𝐞 𝐰𝐢𝐭𝐡 𝐈𝐧𝐝𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧𝐬 𝐚𝐧𝐝 𝐖𝐡𝐢𝐭𝐞𝐬𝐩𝐚𝐜𝐞𝐬 Proper code formatting enhances readability. Consistent indentation and spacing make it easier to follow the code's structure. 𝟒. 𝐑𝐞𝐝𝐮𝐜𝐞 𝐍𝐞𝐬𝐭𝐢𝐧𝐠 Deeply nested code is hard to read and maintain. The recommended practice is try to use not more than 2 levels of nesting. Reducing nesting improves code readability. 𝟓. 𝐑𝐞𝐭𝐮𝐫𝐧 𝐄𝐚𝐫𝐥𝐲 When conditions aren't met - return early from the method and prevent unnecessary code execution. Returning early from the method reduces nesting, and as a result, it improves code readability. 𝟔. 𝐆𝐞𝐭 𝐑𝐢𝐝 𝐨𝐟 𝐄𝐥𝐬𝐞 𝐊𝐞𝐲𝐰𝐨𝐫𝐝 Often when reading code in the else statement, you need to scroll up to see the corresponding if. After you add an early return, an else block becomes unnecessary 𝟕. 𝐀𝐯𝐨𝐢𝐝 𝐝𝐨𝐮𝐛𝐥𝐞 𝐧𝐞𝐠𝐚𝐭𝐢𝐯𝐞𝐬 𝟖. 𝐀𝐯𝐨𝐢𝐝 𝐌𝐚𝐠𝐢𝐜 𝐍𝐮𝐦𝐛𝐞𝐫𝐬 𝐚𝐧𝐝 𝐒𝐭𝐫𝐢𝐧𝐠𝐬 𝟗. 𝐂𝐨𝐧𝐭𝐫𝐨𝐥 𝐍𝐮𝐦𝐛𝐞𝐫 𝐨𝐟 𝐌𝐞𝐭𝐡𝐨𝐝 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬 𝟏𝟎. 𝐀𝐩𝐩𝐥𝐲𝐢𝐧𝐠 𝐭𝐡𝐞 𝐒𝐢𝐧𝐠𝐥𝐞 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞 𝟏𝟏. 𝐂𝐨𝐫𝐫𝐞𝐜𝐭𝐥𝐲 𝐔𝐬𝐞 𝐁𝐫𝐚𝐜𝐞𝐬 { } 𝟏𝟐. 𝐃𝐨 𝐍𝐨𝐭 𝐑𝐞𝐭𝐮𝐫𝐧 𝐍𝐮𝐥𝐥 𝐟𝐨𝐫 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧𝐬 Want to become better at coding? --- ✅ If you like this post - 𝐫𝐞𝐩𝐨𝐬𝐭 to your network and 𝐟𝐨𝐥𝐥𝐨𝐰 me. ✅ Join 𝟑𝟓𝟎𝟎+ software engineers that are already 𝐫𝐞𝐚𝐝𝐢𝐧𝐠 my newsletter and are 𝐢𝐦𝐩𝐫𝐨𝐯𝐢𝐧𝐠 their .NET and architectural skills --- #csharp #dotnet #cleancode #refactoring #programming #softwareengineering #softwaredevelopment #bestpractices #softwaredesign

10 Rules Every Developer Should Follow to Write Clean Code 👇 Many bugs aren’t caused by complex logic but by unclear, messy code. Clean code isn’t about being clever. It’s about being clear, predictable, and easy to change. These are the rules engineers rely on when systems grow, teams change, and bugs become expensive. Let’s break it down step by step ⬇️ [𝟭.] 𝗔𝘃𝗼𝗶𝗱 𝗠𝗮𝗴𝗶𝗰 𝗡𝘂𝗺𝗯𝗲𝗿𝘀 𝗮𝗻𝗱 𝗦𝘁𝗿𝗶𝗻𝗴𝘀 Hard-coded values hide intent and fail silently. ◾ Replace them with named constants or enums so the code explains itself. [𝟮.] 𝗨𝘀𝗲 𝗠𝗲𝗮𝗻𝗶𝗻𝗴𝗳𝘂𝗹, 𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝗡𝗮𝗺𝗲𝘀 Names should explain why something exists, not how it works. ◾ If a variable needs a comment, the name is wrong. [𝟯.] 𝗣𝗿𝗲𝗳𝗲𝗿 𝗘𝗮𝗿𝗹𝘆 𝗥𝗲𝘁𝘂𝗿𝗻𝘀 𝗢𝘃𝗲𝗿 𝗗𝗲𝗲𝗽 𝗡𝗲𝘀𝘁𝗶𝗻𝗴 Deep nesting increases cognitive load and hides edge cases. ◾ Guard against invalid states early — flat code is readable code. [𝟰.] 𝗔𝘃𝗼𝗶𝗱 𝗟𝗼𝗻𝗴 𝗣𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿 𝗟𝗶𝘀𝘁𝘀 Too many parameters signal unclear responsibilities. ◾ Group related values into objects, records, or configuration models. [𝟱.] 𝗞𝗲𝗲𝗽 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗦𝗺𝗮𝗹𝗹 𝗮𝗻𝗱 𝗙𝗼𝗰𝘂𝘀𝗲𝗱 A function should do one thing well. ◾ If you can’t describe it in one sentence, split it. [𝟲.] 𝗞𝗲𝗲𝗽 𝗖𝗼𝗱𝗲 𝗗𝗥𝗬 Repeated logic means repeated bugs. ◾ Extract shared behavior instead of copy-pasting. [𝟳.] 𝗔𝗽𝗽𝗹𝘆 𝘁𝗵𝗲 𝗞𝗜𝗦𝗦 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 Simple beats clever every time. ◾ Clever code impresses. Simple code survives. [𝟴.] 𝗣𝗿𝗲𝗳𝗲𝗿 𝗖𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 𝗢𝘃𝗲𝗿 𝗜𝗻𝗵𝗲𝗿𝗶𝘁𝗮𝗻𝗰𝗲 Inheritance increases coupling and rigidity. ◾ Use composition to add behavior without locking designs. [𝟵.] 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 𝗢𝗻𝗹𝘆 𝗪𝗵𝗲𝗻 𝗡𝗲𝗰𝗲𝘀𝘀𝗮𝗿𝘆 Good code explains what. Comments should explain why. ◾ If comments explain the code, refactor the code. [𝟭𝟬.] 𝗪𝗿𝗶𝘁𝗲 𝗚𝗼𝗼𝗱 𝗖𝗼𝗺𝗺𝗶𝘁 𝗠𝗲𝘀𝘀𝗮𝗴𝗲𝘀 Commits are part of your documentation. ◾ Explain what changed and why — future you will thank present you. 💡 Takeaway Clean code scales better than clever code. You don’t pay for messy code today; you pay for it later, with interest. Over to you 👇 Which rule did you learn the hard way? ────────── I help developers and teams build scalable backend systems with Django, Spring Boot, Odoo, and AI integration. Let’s work together. ♻️ Reshare if helpful ✅ Follow Jafar Muzeyin 📺 Watch more on my YouTube channel: [https://lnkd.in/eBmq2kuY] Reference -> algomaster

7 Simple Ways to Write Clean Code (Without Overengineering): 0. Meaningful names: 🔴 data, obj, temp, x give zero context. ✅ Use names with intention, like userList, shoppingCart, or isBuyValid. 1. Small functions, single purpose: 🔴 A 200-line function that does everything from validation to sending emails. ✅ Write functions that do one thing. If you can name it with "and," it's doing too much. 2. Consistent style and linting: 🔴 Inconsistent formatting makes code hard to read and review. ✅ Use a linter (ESLint, Prettier) to automate consistency and catch subtle bugs. 3. Don't repeat yourself (DRY): 🔴 The same validation logic is copied in 12 different places. ✅ Abstract repeated code into a single function, module, or class. 4. Comment the why, not the what: 🔴 Comments like i++ // increment i are just noise. ✅ Use comments to explain confusing business logic or the reason behind a complex decision. 5. Avoid deep nesting: 🔴 "Callback hell" or 10 levels of if statements are impossible to follow. ✅ Return early, use promises/async-await, and flatten your code structure. 6. Write tests: 🔴 You're afraid to change code because you don't know what will break. ✅ Unit tests act as a safety net and documentation for how your code is supposed to work. What did I miss?