Best Practices for Code Refactoring

I'm a Software Engineer at AWS, and here are 18 lessons I learned about refactoring code over the last 7 years in 60 seconds. It took me a lot of mistakes to learn these, so you don't have to: 1/ Never assume how code behaves → verify it with tests before changing anything 2/ Refactor in small, reversible steps → big rewrites break things. 3/ Don't change too much at once → reduce the blast radius 4/ Use AI as a refactoring partner → set guardrails, verify with tests, and iterate in small steps 5/ Test before refactors → they protect behaviour, not implementations. 6/ Keep it simple (KISS) → most complexity is accidental 7/ Fix design problems, not symptoms → good architecture prevents bugs 8/ Keep your code DRY → duplication multiplies risk 9/ Performance matters → refactoring isn't just structure, it's behaviour at scale 10/ Legacy code isn't scary → changing it blindly is 11/ Know your goal before refactoring → clarity beats activity 12/ Readable code beats clever code → readable code is easy to maintain in production 13/ Favour composition over inheritance → inheritance adds more complexity 14/ Patterns aren't always your friend → context matters more than theory 15/ Code is for humans → future readers are part of your system 16/ Refactoring is a habit → it's how systems stay healthy over time and avoid "broken windows" 17/ Messy code is a liability → technical debt compounds quietly. 18/ Refactor the code you touch most → optimise for where teams spend time. P.S. What else would you add? --- 🔖 Save this for the next time you're about to "just clean it up" ➕ Follow Abdirahman Jama for practical software engineering tips. #softwareengineering

Don’t break your code during refactoring - there’s a better way. One of my go-to refactoring techniques is Parallel Change. It’s the same concept used in road construction: instead of blocking an entire street until the work is done, you build a detour to keep traffic flowing. Similarly, with Parallel Change, your code continues to function while changes are being implemented. If you’re new to this technique, start small. Practice with simple examples or katas to understand how it works. As you gain confidence, apply it to your day-to-day work - it’s a great way to develop the habit of keeping your code functional throughout the process. When dealing with modernization or legacy projects, this method proves its value even more. It eliminates the headache of fixing broken, non-compiling spaghetti code, allowing you to commit anytime and pause your work without worry. Mastering Parallel Change can make refactoring smoother, safer, and far less stressful. Give it a try - you’ll never want to go back to dealing with broken code.

🚨 When transformation logic is spread all over the repository, it becomes a nightmare to modify, debug, and test. This scattered approach leads to duplicated code, inconsistencies, and a significant increase in maintenance time. Developers waste precious hours searching for where transformations occur, leading to frustration and decreased productivity. 🔮 Imagine having a single place to check for each column's transformation logic—everything is colocated and organized. This setup makes it quick to debug, simple to modify, and easy to maintain. No more digging through multiple files or functions; you know exactly where to go to understand or change how data is transformed. 🔧 The solution is to create one function per column and write extensive tests for each function. 👇 1. One Function Per Column: By encapsulating all transformation logic for a specific column into a single function, you achieve modularity and clarity. Each function becomes the authoritative source for how a column is transformed, making it easy to locate and update logic without unintended side effects elsewhere in the codebase. 2. Extensive Tests for Each Function: Writing thorough tests ensures that each transformation works as intended and continues to do so as the code evolves. Tests help catch bugs early, provide documentation for how the function should behave, and give you confidence when making changes. By organizing your code with dedicated functions and supporting them with robust tests, you create a codebase that's easier to work with, more reliable, and ready to scale. --- Transform your codebase into a well-organized, efficient machine. Embrace modular functions and comprehensive testing for faster development and happier developers. #CodeQuality #SoftwareEngineering #BestPractices #CleanCode #Testing #dataengineering

The next code smell I want to highlight is one of my personal pet peeves, Primitive Obsession. This means using a primitive or String for a value just because it sort-of fits, rather than using a well-designed object. Yes, there is effort in creating the object, but that effort pays off many times over. What do I mean? How about using a float to represent money just because it has a decimal point. That *always* causes problems. You always have to deal with imprecision and rounding errors. You need a Money class that can store exact values, handle multiple currencies and perform conversions. Another horrible idea is to use a String (or even worse, an int) to hold a phone number. You often need to parse out a country code or an exchange by splitting the String or dividing the int. You can express these concepts directly in a TelephoneNumber class. The same goes for using String to represent an address. Use an Address object to hold a name (which should also be an object), a house number, street name, apartment number, city, province. Postal code and country. The same goes for dates, times, ranges and other types. Of course, Java has already defined classes for those. Any time you have a value in a primitive and keep having to split it apart to use it, you really need to define an object.The appropriate refactoring is “Replace Data Value With Object”. Sometimes, we use a primitive to represent a type code, which can also cause problems. You should use an enum or a class. If you don’t depend on the type code to affect behavior of the system, use “Replace Type Code With Class.” If you do use it for branching, then you can consider “Replace Type Code With Subclasses”, or “Replace Type Code With State/Strategy”. Another smell is storing parts of a value in an Array, then having to pick out pieces by knowing the indices of those pieces. If you find yourself doing this, you can use “Replace Array With Object”. Sometimes, you might have pieces of a value in individual primitive fields and parameters. In this case, you can put them together with “Extract Class” or “Introduce Parameter Object.” Shoe-horning complex values into a primitive is always a bad idea. You may think it is “easier” or “less work”, but it always comes back to bite you. You can find descriptions of the refactorings at https://lnkd.in/gdJjNQcF

Legacy code: it’s a mess. No one wants to touch it. But it pays the bills. You open a file and it’s like walking into a maze: → No comments. → 300-line functions. → Variable names like ‘temp3’ and ‘doSomething()’. It’s a nightmare. But here’s the reality: most of us don’t get to start fresh. The code works, and rewriting it isn’t practical. Your job? Make it better without breaking it. Here’s how you can approach it: 1. Understand before you refactor. Don’t just dive in and start deleting things. Read it. Map it out. Use tools to speed this up. Ex - Bito can summarize logic or explain what a function or entire files does in plain English. Saves hours. 2. Write tests first. If there are no tests, you’re flying blind. Write some coverage before you change anything, so you know if it breaks. 3. Fix small, high-leverage things. → Rename variables (’temp3’ → ’averageTemp’). → Split up massive functions. → Add comments where the logic is dense. Small changes compound over time. 4. Leave it better than you found it. If you struggled to figure something out, document it. Add a test. Refactor the worst parts. Legacy code is how we got here… it’s alive, it’s evolving. Don’t hate it. Maintain it. And when you’ve got the right tools, the process doesn’t have to be painful. I’ve seen teams clean up years of spaghetti with AI tools that: → Identify unclear code. → Suggest refactors. → Catch bugs early. The goal isn’t to “modernize” everything. It’s to make legacy code easier to extend, understand, and trust. Fix what matters. Move fast. Don’t break things. #bug #code #ai #developer

Here’s how to clean up messy frontend and backend code—for good: ☑ Frontend Refactoring (React, Vue, Svelte) 1. Modularize the UI → Break large components into smaller, reusable ones → Use component libraries (like Storybook) to enforce consistency 2. Tame the State Monster → Ditch prop drilling for Context, Zustand, or Vuex → Clean up unused state and only lift when necessary 3. Prevent Useless Renders → Use memo, useMemo, useCallback, or Vue’s computed → Code-split and lazy load for faster UX 4. Modernize Your Styles → Try Tailwind, CSS-in-JS, or scoped styles in Vue → Remove dead CSS and centralize theme tokens 5. Lint It or Regret It → Add ESLint, Prettier, and TypeScript for sanity → Automate formatting in CI 6. Shrink Your Bundle → Enable tree shaking, dynamic imports, and minification → Tools like Vite, Webpack, or esbuild make it easy — ☑ Backend Refactoring (Node, Express, Nest, Django, Rails) 1. Modular by Design → Use MVC or Hexagonal to separate concerns → Prefer grouping by feature, not layers 2. Service Layer FTW → Keep business logic out of controllers → Easier to test, scale, and maintain 3. Database Speed Boost → Add indexes, cache smartly (Redis), and avoid N+1 queries → Normalize or denormalize based on query patterns 4. Make Your APIs a Joy to Use → Use pagination, filtering, and proper HTTP status codes → REST is great—but consider GraphQL for flexibility 5. Go Async or Go Home → Offload heavy work to queues (Bull, Sidekiq, Celery) → Use async/await where I/O blocking hurts 6. Test. Then Test Again. → Split large functions into testable units → Write unit + integration tests and run them on every commit — Cross-Cutting Musts → Atomic commits with clear messages → API docs with Swagger or JSDoc → Automate tests and linting with CI/CD — Refactoring isn’t just technical debt cleanup—it’s developer self-respect. If this helped, repost to help clean up someone else’s codebase too. Or DM me if you want to apply these to your stack.

Do you refactor the code or queries you create for your data analytics tasks? As data analysts, we often need to deliver insights quickly for our stakeholders. But ignoring the refactoring process can lead to technical debt and inefficiencies that hurt your long-term productivity. 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗼𝗳 𝗥𝗲𝗳𝗮𝗰𝘁𝗼𝗿𝗶𝗻𝗴: • Enhanced Readability    • Improved Performance    • Simplified Maintenance    • Reduced Errors    • Better Scalability 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝘀𝗲𝘀 𝗳𝗼𝗿 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗥𝗲𝗳𝗮𝗰𝘁𝗼𝗿𝗶𝗻𝗴: 1. Simplify Complex Queries and Code Blocks     2. Adopt Consistent Naming Conventions     3. Remove Redundancies     4. Comment and Document     5. Use Version Control     6. Test Your Changes Incorporate regular refactoring into your workflow. Set aside time after the initial development to revisit and improve your code. This practice will enhance the quality of your current projects and present you as an experienced data professional. Is refactoring a part of your data analytics process? ---------------- ♻️ 𝗦𝗵𝗮𝗿𝗲 if you find this post useful ➕ 𝗙𝗼𝗹𝗹𝗼𝘄 for more daily insights on how to grow your career in the data field #dataanalytics #datascience #codequality #refactoring #python #careergrowth

🔥 𝗦𝗢𝗟𝗜𝗗 𝗶𝗻 𝗔𝗻𝗱𝗿𝗼𝗶𝗱 — 𝗳𝗿𝗼𝗺 𝗯𝘂𝘇𝘇𝘄𝗼𝗿𝗱 𝘁𝗼 𝗰𝗹𝗲𝗮𝗻 𝗰𝗼𝗱𝗲 Most Android projects become harder to maintain as they grow—ViewModels overloaded, messy dependencies, bloated interfaces. That’s where SOLID principles rescue us. Let’s see how each applies to Android dev (with real examples). 👇 Problem: ViewModels doing UI + business logic + network + caching. Solution: Extract UseCases (e.g. LoginUseCase) → ViewModel only exposes UI state. ✅ O – Open/Closed Problem: Adding new screen states = editing enums & when blocks everywhere. Solution: Use sealed classes → add new states without touching existing code. ✅ L – Liskov Substitution Problem: Subclasses break expectations (override badly, throw errors). Solution: Define interfaces for behavior, use careful base classes (e.g. Fragments). ✅ I – Interface Segregation Problem: Huge interfaces force unused methods (e.g. adapters with click + long click + swipe). Solution: Split into smaller contracts → classes only implement what they need. ✅ D – Dependency Inversion Problem: ViewModels depending directly on concrete Repositories/DataSources → tightly coupled, hard to test. Solution: Depend on interfaces/abstractions instead of concrete classes. Inject the implementation (via constructor or DI tools like Hilt/Dagger/Koin). 💡 Why It Matters Testable code → easier mocking. Maintainable structure → safe refactors. Scalable apps → adding features without breaking others. Clean separation → everyone in the team codes with confidence. 📌 Try this today: Pick your largest ViewModel → extract one UseCase + one interface. You’ll feel the difference immediately. ⚡ 👉 Question: Which SOLID principle do you struggle most to apply in your Android projects? Drop it in comments—I’ll share some practical refactoring examples. #AndroidDev #Kotlin #CleanArchitecture #MVVM #SOLID #DeveloperTools #MobileDev #CodeQuality #DesignPatterns

One of the best advice I’ve received from a senior early in my career was to read Clean Code by Robert C. This is one of the most impactful books I’ve ever read. It forever changed how I used to code. If I had to summarize the 10 most important principles from the book, they would be: 1. Meaningful Names - Choose clear, descriptive names that reveal the intent of your code. - Names should help others understand the purpose without extra context. - Example: Use `totalCost` instead of `x` for clarity. 2. Small Functions - Keep functions small and focused on a single task. - If a function exceeds 20 lines, consider refactoring. - Example: A `calculateTotal()` function should only handle calculations, not logging. 3. DRY Principle (Don’t Repeat Yourself) - Avoid code duplication to reduce maintenance complexity and potential bugs. - Aim for reusability and modularity in your code. - Example: Use a `processUserInput()` function rather than repeating the same logic multiple times. 4. Avoid Comments - Write self-explanatory code to minimize the need for comments. - Outdated comments can mislead, so focus on making the code itself clear. - Example: Refactor a complicated `for` loop into a well-named function rather than explaining it with comments. 5. Error Handling - Separate error handling from business logic to keep code clean. - Handle exceptions gracefully to maintain resilience. - Example: Use a `try-catch` block around critical operations and log errors in a dedicated function. 6. Readable Code - Prioritize readability over cleverness to make the code easy to understand. - Consistent formatting and naming conventions enhance code clarity. - Example: Use clear indentation and consistent variable names like `userName` and `userAge`. 7. Single Responsibility Principle (SRP) - Ensure each class and function has one responsibility or reason to change. - This principle makes the code more modular and easier to test. - Example: A `User` class should only handle user-related data, not database operations. 8. Dependency Injection - Rely on interfaces or abstractions rather than concrete implementations. - This approach decouples components and makes the code more flexible and testable. - Example: Inject a `PaymentProcessor` interface into a `Checkout` class rather than using a specific payment gateway directly. 9. Testing - Write automated tests to validate your code and catch bugs early. - Tests act as a safety net, ensuring code behaves as expected after changes. - Example: Use unit tests to verify that a `calculateDiscount()` function returns the correct value for various input scenarios. 10. Refactoring - Continuously improve your code through refactoring to maintain quality. - Refactoring should be an ongoing process, not a one-time task. - Example: Regularly revisit old code to simplify logic or reduce duplication, like merging similar methods into one.