Refactoring Techniques for Confident Code Updates

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.

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

AI + TDD = Conquering Legacy Code With Confidence, Not Fear Ever stare at a 1,000+ line function with deeply nested branching logic and a hundred parameters with no automated tests? Most developers would run from that code. As software engineers majority of the job is reading and understanding the code we already have. Many times it's a version of the code described above. It’s even worse when you wrote it years ago and can’t remember what it does anymore. The good news is that there is a safe way through the mess. What is it? 👉 AI + TDD working together. Not "Refactor this" or "make this readable", that's just guaranteed way to generate more slop even faster. I use AI and TDD to help me understand the code and proceed in small, safe, reversible steps Here’s the process that’s been a game-changer for me: 1. First, use AI to understand the code I ask AI to read the code, analyze the logic, and explain what’s happening. Then I ask follow-up questions to validate its interpretation against the actual code. This allows me to understand the code and not get overwhelmed. I can ask questions and verify that what the AI tools me is correct. 2. Use AI to design a safe refactoring path Inspired by Michael Feathers’ "Working Effectively with Legacy Code", I have AI help me: ✅️ Find test seams. ✅️ Outline small extraction steps. ✅️ Highlight risky logic. ✅️ Map hidden dependencies. ✅️ Design a requirements + testing plan. This gives me confidence that I have a clear path forward, not guesswork. Pro Tip have the AI create a snapshot of this understand into a markdown file in a log folder so you ensure you don't lose this valuable context. 3. Use TDD to move only one piece at a time Before touching anything, I write a test that captures the behavior of the small part I want to modify or move. Once that test fails (by design), I ask AI to help create the smallest code change possible to make it pass. Then I rinse and repeat. Tiny steps, full safety, no big-bang rewrites. This creates compounding confidence: Every green test is proof that the code I and my AI assistant has added works. It’s the best combination of relief (“I’m not overwhelmed anymore”) and confidence (“I can do this safely”) I’ve found for working in complex systems.

If you have a React app with a lot of legacy code that is hard to work with and risky to touch, here's the approach I'd take: 1) Whenever a new business requirement lands on your table, try to figure out what code you have to touch to implement it. 2) Figure out an existing software behavior the target code represents. Take some notes about it. 3) Write a high-level acceptance test asserting the software behavior you figured out. It could be an integration or E2E/M test that boots the app (or renders a specific page component), mocks API endpoints, and captures a particular user journey or multiple journeys. This step is critical because having user journeys automated provides us confidence in refactoring the underlying code. 4) Refactor the existing code — strive to make it less coupled, increase cohesion, and split it into multiple functional layers. 5) Write another high-level test covering a new business requirement. It will be red, failing test. That's fine. Important is that it clarifies for you the desired software behavior to implement. Take it as a DoD (Definition of Done) for yourself. 6) Now, touch your refactored production code to incorporate a new business requirement until the failing acceptance test passes. That's the routine, a habit to build. Don't consider solving tech debt something separate from a daily job. Embed refactoring into feature delivery to have synergy between delivering business value and improving the existing code for future changes. #reactjs #tdd #refactoring

After years of struggling with when and what to refactor, our team discovered a simple approach that's transformed how we handle technical debt. Instead of trying to fix everything or having endless debates about code quality, we ask one question: "What's making it harder to implement our next feature?" This strategy gives us:   • Clear priorities for refactoring efforts   • Faster feature development   • Focus on code that's actively changing   • Immediate business value from improvements The beauty is in its simplicity. We're not refactoring for the sake of refactoring - we're removing specific obstacles that slow us down right now. By targeting code that needs to change anyway, we get double value: the new feature gets implemented faster, and the codebase becomes more maintainable in areas that matter. Sometimes the most effective solutions are the ones that align improvement work with immediate business needs. What refactoring strategies have worked for your team? #SoftwareDevelopment #Refactoring #TechnicalDebt #CodeQuality #TeamProductivity