Bug Fixing in Open Source

Fixing a bug is only half the battle. These critical questions must be answered for every defect or else there is room for failure 1. Root Cause: What exactly caused the bug? 2. Collateral Impact: Does the fix potentially affect other areas of code or functionality? 3. Origin: Why did the bug occur—was it a missed edge case, oversight during code review, or a missing test case? Room for process improvement here. 4. Prevention: What measures are in place to ensure this problem never happens again? Process improvement, better documentation etc 5. Monitoring: Until a permanent fix is implemented, what systems are monitoring for recurrence? 6. Data Collection: Is there a need to collect more information (e.g., additional logs) for better troubleshooting in the future? Too often, teams skip this deep-dive process, ending up in a constant firefight mode, fixing issues repeatedly without addressing the underlying problem. Every defect requires a thorough investigation—there are no shortcuts to building lasting, high-quality code. What if there is no time to do all this ? At least do it for your showstoppers At least do it once in 2 weeks There is always a way if there is intent And something is better than nothing Slowly but surely this would lead to codebase that stands the test of time. No shortcuts. Just excellence.

I used AI to mutation-test my open-source library. The results were chilling. My FAIR library (Go, open source) proudly shows 96.1% coverage. That sounds solid, right? Except coverage is a terrible quality metric. It just tells you “this line ran once,” not “your tests would actually catch a bug.” So I tried something new: AI-powered mutation testing. - Asked GPT-5 to generate 40 small, realistic bugs: off-by-one errors, nil checks skipped, expiry math off by a fencepost, that kind of thing. - Each mutant compiled and actually changed behavior (not just cosmetic stuff or non changes like min(a, b) vs min(b, a)). - Ran my full test suite against them individually. Result: Only 15/40 mutants were caught. That’s a 37.5% mutation score on a library with 96.1% coverage. Let that sink in! Some of the survivors were… embarrassing. (I’m usually the one lecturing others about missing asserts.) Turns out my tests were more “touch the code and hope” than “lock in behavior.” The interesting bit: GPT-5 was really good at this. I tried Claude before and got a bunch of invalid/equivalent edits. GPT-5 cranked out believable bugs that looked like the kind of mistakes a rushed engineer would make in code review. I’m now going back and writing tests for the survivors. That’ll be a follow-up post. For now, if you want to scare yourself a little, try this on your own repo. Prompt: https://lnkd.in/gFkSMR8f Mutations dir: https://lnkd.in/gVT-3bbc Repo: https://lnkd.in/genvvmqz Feel free to steal my prompt (I appreciate if you can tag me though so I can learn). Ask Cursor to generate mutants using it in batches of 10 using gpt-5. Bonus points if you can turn this into a repeatable GitHub step or library. Also, I appreciate ☆ if you find it useful. Despite the current state of testing, it's a very useful library ;) .

Anyone can fix a bug. But the way you do it shows what kind of engineer you are. Here’s a checklist mindset that’s helped me: ✅ Try to reproduce the bug first ✅ Trace where in the codebase it’s happening ✅ Backtrack the logic & data flow - understand the “why” ✅ Figure out what files or components need changes ✅ Plan how you’ll verify if your fix actually works ✅ If you’re stuck, ask questions early (not last!) ✅ Once fixed, check if it’s working end-to-end ✅ Write tests to catch it early in the future ✅ Follow through: share updates, close loops, and let people know it’s taken care of - that’s how you build trust. You didn’t just solve a bug. You solved it well.

We found a "silent killer" in one of the world’s most popular tech frameworks. Most would have just hit restart. We fixed the source code for everyone. At ClickPost, we use OpenTelemetry to keep our systems bulletproof. But during a recent rollout, things went dark. Rather than just lagging, our systems silently froze. It wasn’t a bug in our app, but a flaw deep inside the global #Python SDK used by millions of developers worldwide. Instead of waiting for a fix, our engineers performed "open-heart surgery" on the framework. We traced the root cause, wrote the solution, and after 4 rounds of global peer review, our fix was officially merged into the OpenTelemetry project. When you operate at our scale, you don't just use tech, you help build it. If your Python systems have been freezing mysteriously, we just solved that for you. Link to the tech blog in comments. #EngineeringExcellence #OpenSource #ClickPost #TechLeadership