4-Step Debugging Process for Developers

The 4-Step Smart Debugging Process! Good developers fix bugs. Great developers find the real cause quickly. Instead of randomly changing code, follow this simple process: 1️⃣ Reproduce the Problem: Make the bug happen again. > Same steps. > Same environment. 📌 If you can’t reproduce it, you can’t fix it. 2️⃣ Isolate the Cause: Narrow down where the issue happens. Check: * inputs, * recent changes, * dependencies. 📌 Find the exact point of failure. 3️⃣ Fix the Root Cause: > Don’t just patch the symptom. > Understand why the bug happened and correct the logic. 📌 Temporary fixes create future bugs. 4️⃣ Prevent It From Returning: Add: ✔ tests, ✔ logs, ✔ validation. 📌 Good fixes also prevent future problems. 💡 Insight! Random debugging wastes hours. A structured debugging process saves time and builds stronger systems. #Debugging #CodingBestPractices #TechProductivity #GeekAxon

𝗪𝗵𝘆 𝗠𝗼𝘀𝘁 𝗗𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝗘𝗳𝗳𝗼𝗿𝘁𝘀 𝗙𝗮𝗶𝗹 ⸻ We spent hours debugging… and we were looking in the wrong place. ⸻ 💡 What usually happens: • Engineers debug symptoms, not root cause • Logs can be misleading • Assumptions waste time ⸻ 🧠 Core insight: 👉 Debugging is about asking the right question, not searching more logs ⸻ A recent incident reminded me of this. We had a failure in a QR code generation flow for a tenant. At first, everything looked correct: • feature was enabled • configuration seemed valid • another implementation had worked earlier So naturally, we assumed: 👉 the issue must be in the new implementation ⸻ We went deeper: • checked API flow • reviewed logs • compared implementations Everything pointed in one direction… But it was the wrong one. ⸻ The actual issue? 👉 A hidden tenant-level configuration. A fallback feature was configured directly for that tenant. So even after adding the correct implementation: • the system still picked the fallback • the expected flow never executed • and the error persisted ⸻ The tricky part? This wasn’t obvious in the code. It was buried in configuration. ⸻ That’s when the question changed: ❌ “Where is the bug in the code?” ✅ “What path is the system actually taking?” ⸻ And that’s where we found it. ⸻ 𝗗𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝗿𝗲𝗮𝗱𝗶𝗻𝗴 𝗺𝗼𝗿𝗲 𝗹𝗼𝗴𝘀. 𝗜𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝗮𝘀𝗸𝗶𝗻𝗴 𝗯𝗲𝘁𝘁𝗲𝗿 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀. ⸻ Curious: 👉 What’s the most misleading bug you’ve debugged? ⸻ #SoftwareEngineering #Debugging #BackendEngineering #Microservices #EngineeringLessons

𝗛𝗼𝘄 𝗜 𝗱𝗲𝗯𝘂𝗴 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗶𝘀𝘀𝘂𝗲𝘀 (𝗹𝗲𝗮𝗿𝗻𝗲𝗱 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱 𝘄𝗮𝘆): Most developers jump straight to the code. I used to do this too. Now I start with questions: 𝗪𝗵𝗮𝘁 𝗰𝗵𝗮𝗻𝗴𝗲𝗱? If it worked yesterday and broke today, something changed. Find that thing first. Usually it's not the code - it's data, environment, or user behavior. 𝗖𝗮𝗻 𝗜 𝗿𝗲𝗽𝗿𝗼𝗱𝘂𝗰𝗲 𝗶𝘁? If I can't make it break on command, I don't understand it yet. "Sometimes it happens" means keep digging. 𝗪𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝘀𝗺𝗮𝗹𝗹𝗲𝘀𝘁 𝘁𝗵𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝗰𝗼𝘂𝗹𝗱 𝗰𝗮𝘂𝘀𝗲 𝘁𝗵𝗶𝘀? Complex systems break in simple ways. Page flickering? Usually one useEffect with bad dependencies. API failing? Usually one misconfigured header. 𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝘁𝗵𝗲 𝘂𝘀𝗲𝗿 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘀𝗲𝗲? Error messages lie. Logs lie. What users experience is truth. The best debugging isn't the fastest fix. It's understanding the problem correctly first. #SoftwareEngineering #Debugging #WebDevelopment #React

One debugging practice almost nobody talks about: reviewing your code as different personas, not just a peer. Most debugging workflows look similar on the surface. What’s different here is the part we rarely talk about: how reviewing your own code through multiple personas exposes blind spots that normal peer review misses. I wrote a short piece about it: including the 7‑step workflow I use and the structural failures that usually hide underneath. Medium post: https://lnkd.in/gi8RHkga #SoftwareEngineering #Debugging #CodeReview #EngineeringLeadership #SoftwareArchitecture

A small production bug taught me a big lesson today. Everything was working perfectly in my local environment. Queries ✔️ API response ✔️ Logic ✔️ After deployment… production started behaving differently. At first, it felt like one of those classic developer moments: Works on local → 😎 Deploy to production → 😐 Unexpected issue → 😰 After digging into logs and debugging the queries, the issue turned out to be something very small: a data formatting edge case that wasn’t present in local data but existed in production. The fix took only a few minutes. The debugging took hours. That’s when I realized something important about software development: Writing code is only part of the job. Understanding systems, logs, data, and edge cases is where real engineering happens. Every production issue is frustrating in the moment but valuable in the long run. Curious to know from other developers here: What’s the smallest bug that caused you the biggest headache? 👀 #SoftwareEngineering #BackendDevelopment #Debugging #Developers #TechLife #Engineering #Blocker #ProblemSolving

💡 𝐃𝐞𝐛𝐮𝐠𝐠𝐢𝐧𝐠 𝐓𝐢𝐩𝐬 & 𝐓𝐫𝐢𝐜𝐤𝐬  Debugging is not just fixing errors — it’s a structured problem-solving skill. Here are key tips every developer should follow: 🔹 1. Reproduce the Issue First Always recreate the bug. Never rely on assumptions — real observation gives real insights. 🔹 2. Break It Down Divide your code into smaller parts and test each section to isolate the issue faster. 🔹 3. Focus on Root Cause (Not Symptoms) Fixing symptoms leads to repeated bugs. Always identify why it happened. 🔹 4. Use Logs & Debug Tools Leverage IDE debuggers, logs, and breakpoints to track execution and variables. 🔹 5. Apply Smart Debugging Techniques Try methods like backtracking, hypothesis testing, or even explaining your code. 🔹 6. Test Thoroughly After Fixing Run unit, integration, and regression tests to ensure stability. 🔹 7. Document Your Fixes Write down what caused the issue and how you solved it. This builds a knowledge base for future problems and improves team efficiency. Regards Ali Sena Danishwer

Day 279 : The best testers I’ve worked with weren’t the fastest at writing tests. They were the best at debugging. Anyone can learn: • a tool • a framework • a syntax But debugging? That’s where real skill shows up. Because when something fails, you’re not just fixing code. You’re answering: • What actually happened? • Where did the system behave differently than expected? • Is this a test issue, data issue, or system issue? I’ve seen teams spend hours: • rerunning tests • blaming flakiness • tweaking waits …instead of understanding the root cause. The shift that made a difference: 👉 Treat every failure as an investigation, not a task. What good debuggers do differently: 🔹 Reproduce before fixing 🔹 Break the problem into layers (UI → API → data) 🔹 Question assumptions instead of patching symptoms 🔹 Use logs, not guesses Because: Writing tests creates coverage. Debugging creates understanding. And understanding is what actually improves quality. ⸻ If you had to choose one — better at writing tests or better at debugging — what would you pick? #SoftwareTesting #TestAutomation #QualityEngineering #SDET #Debugging #EngineeringSkills #AutomationTesting

The Claude Code leak is interesting, but practically speaking, it's just not relevant for most users. What I really want to see is your org's custom skill stack. You can customise quite a lot of agent behaviour using skills. You can encode your own expectations and preferences, and even patch gaps in the agent's harness. On that note, I found a few weak spots in the harness that I thought I'd try to address. The cool thing about using Tessl for this is that when/if these behaviours get absorbed into the harness officially, you won't need a source code leak to notice. You’ll just see that the eval uplift from those skills disappears compared to the baseline. Here are the skills I published: 𝐜𝐨𝐦𝐩𝐚𝐜𝐭-𝐝𝐞𝐛𝐮𝐠-𝐥𝐞𝐝𝐠𝐞𝐫 - 3.66x eval uplift Turns a messy debugging thread into a structured ledger of evidence, attempts, ruled-out hypotheses, and next experiments. Use it when a debugging session has gone long, and you need to resume, hand off, or proactively compact. 𝐫𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧-𝐬𝐜𝐨𝐮𝐭 - 2.72x eval uplift Looks for nearby breakage after a change instead of just re-checking the main fix. Use it when you want to know if anything 𝘦𝘭𝘴𝘦 might have broken. 𝐜𝐨𝐦𝐩𝐚𝐜𝐭-𝐡𝐚𝐧𝐝𝐨𝐟𝐟 - 1.41x eval uplift Produces a continuation-ready handoff with objective, completed work, assumptions, unresolved issues, and next action. Use it when another party needs to pick up without rereading the whole thread. Patches some small gaps in CC's native compaction behaviour. 𝐬𝐤𝐞𝐩𝐭𝐢𝐜-𝐯𝐞𝐫𝐢𝐟𝐢𝐞𝐫 - 1.3x eval uplift Shifts verification into an adversarial mode: try to falsify the claim, gather evidence, and return PASS, PARTIAL, or FAIL. Use it when you want the agent to try and break something, not just passively review. In the comments, I'll link to my Tessl workspace where you can find & download these skills (without even signing up, btw).

Most developers chase new frameworks. Very few master debugging. And that’s the difference between a good engineer and a great one. In my experience, the most overlooked part of debugging is thinking, not tooling. People jump into: - console.log - breakpoints - stack traces But skip the most important step: 👉 Understanding the system before touching the code Great debuggers: - Reproduce the issue consistently - Form hypotheses before changing anything - Trace the data flow, not just the error Debugging is not about fixing bugs. It’s about understanding why the system behaves the way it does. What do you think is the most underrated debugging skill? #SoftwareDevelopment #Debugging #CareerGrowth

You’re debugging a system. The output isn’t what you expect. Nothing is failing, no errors and no alerts. It just…doesn’t make sense. So you start with the easiest thing first. You look at the payload, open the request, check the fields and compare it to what you expected to send. Maybe something is missing, maybe something is shaped differently. Nothing obvious. So you check configuration, feature flags, environment variables. Any toggles that could change behavior. Everything is set the way it should be. Next, you look at the workflow: How was this triggered? What path did it take? What conditions were evaluated? You step through the flow., validate each handoff. Still nothing. Now you widen the search. Maybe another service is involved, something async changed state earlier or timing is affecting the outcome. So you trace across calls, service to service and request to request. Trying to reconstruct the sequence. Everything lines up so you go deeper. You inspect the data itself. Compare it to previous runs and look for edge cases. Anything that could be interpreted differently. Nothing stands out. At this point, you’ve ruled out everything simple, so you open the code. Walk through the logic line by line, follow every branch and check every condition. Even here everything looks correct. Individually, every part makes sense which is what makes it worse. Because when you step back and look at the outcome as a whole…no one can explain it. Not engineering. Not product. Not the business. Nothing is broken; but no one can answer a simple question, why did it do that? That’s when it shifts. You realize you’re not debugging a failure. You’re trying to understand a decision. And there’s no single place where that decision lives. It’s spread across: • services • workflows • configs • prompts • people Each piece contributes. No piece owns it. So the system works…but no one actually understands how it behaves. Once that happens, something changes., you’re not debugging anymore. You’re guessing. Forming theories, testing assumptions…hoping something points you in the right direction. Because the one thing you need…a clear, traceable reason for the outcome…doesn’t exist. That’s the point where systems stop being predictable. Not because they fail…but because no one owns the decision anymore. And when no one owns the decision, you’re no longer designing the system…you’re reacting to it. And once you’re reacting instead of designing, you’re already behind the system you built. What’s the hardest system you’ve had to debug where everything looked right…but the outcome made no sense?