Value of Debugging Skills for Software Engineers

Why Debugging is Your Superpower in the AI-Assisted Development Era In today's world of AI-assisted coding, some might wonder if fundamental engineering skills become less critical. My strong belief? Quite the opposite, especially for debugging. In fact, strong debugging skills are becoming more valuable than ever. AI tools can write code, but they can't debug complex system interactions with the same depth, precision, and understanding that a skilled engineer can. Effective debugging isn't just about finding errors; it's about deep system comprehension: - Domain Knowledge: Understanding the 'why' behind the features. - System Architecture: Knowing the overall design and how pieces fit together. - Module Roles & Interfaces: Grasping what each component does and how they communicate. And for those in networking and security – this is amplified! You need to visualize the packet or session flow across modules, understand what information each module extracts, and what transformations occur. This holistic view is crucial for tracing issues and finding the root cause. Beyond that, core computer science fundamentals like multithreading, concurrency, mutual exclusion, and scale-out are indispensable for identifying subtle bugs in complex, high-performance systems. My advice to all newcomers and engineers: Expand your horizons. Whenever a new bug is discovered in your wider project, volunteer to fix it. Put in the effort. Taking ownership of bugs, especially early on, is an incredible kickstart to learning more. It might take time for the first few fixes, forcing you to dive into modules and concepts you hadn't focused on before. But over time, this commitment will transform you into a highly effective debugger and, eventually, a great architect. Embrace the bug :). It's your direct path to deeper system mastery and a more valuable career. #Debugging #SoftwareEngineering #CareerGrowth #TechSkills #AIinDev #SystemArchitecture #Networking #Cybersecurity #EngineerLife #ProblemSolving #Aryaka

When you fix a bug and you understand the truth behind it surfacing, you level up as an engineer. For example, it may be tempting to add a null pointer check to fix a crash, but it is 10 times as hard to understand how this null pointer came to be. This is especially true in a large codebase. Experienced programmers often know that this fix is a surface fix and something else downstream will break if the source isn’t discovered. Discovering the source is the challenge, the sweat equity, and going through the motions to dig deep into the workflows and understanding the system’s entry points so well to go to the very root of the problem. When the source is uncovered, the feeling is indescribable. The fix is often one line of code in a file you never thought to look in, as opposed to 150 files.

Tons of candidates ask me what makes engineers successful at Chalk. I’ve thought a lot about it, and I think one surprising predictor of success is skill in debugging. I’ve noticed that the strongest engineers are usually the ones who have spent time building a broad toolbox, so when they encounter particularly sticky bugs, they have many angles to attack them from. What does this look like in practice? In an average week, we spend a ton of time looking at cpu and memory profiles, analyzing tcp captures, thinking about traces, and building new introspection functionality into our platform to tighten the debug loop. Ironically, many of the hardest challenges in our software are related to capturing telemetry without adding unacceptable overhead. If these are the sorts of challenges you love, let’s chat!

𝗔𝗜 𝗖𝗮𝗻 𝗦𝘂𝗴𝗴𝗲𝘀𝘁 𝗙𝗶𝘅𝗲𝘀. 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗦𝘁𝗶𝗹𝗹 𝗗𝗲𝗺𝗮𝗻𝗱𝘀 𝗣𝗿𝗼𝗼𝗳. Today I was debugging an issue reported after one of my merge requests was integrated. I asked Cursor (Claude model) to analyze the code and history. It quickly pointed to the likely cause and even identified when the change appeared in the Git history. Impressive. So I started thinking about reverting to an earlier commit and validating the hypothesis. Classic 𝗴𝗶𝘁 𝗯𝗶𝘀𝗲𝗰𝘁 thinking. The model suggested something else: “Let’s just fix the problem.” And that’s where the real lesson appeared. Not knowing the 𝗿𝗼𝗼𝘁 𝗰𝗮𝘂𝘀𝗲 means you cannot be certain about the 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻. That principle hasn’t changed with AI. And it won’t. A few reminders for all of us building software with AI tools:  • AI can suggest fixes. Engineering still requires root-cause analysis.  • A patch without understanding is just a guess.  • Debugging still starts with isolating the change.  • Git bisect is still one of the most powerful debugging tools we have.  • And the only real truth in software delivery is the CI pipeline. One more thing from the same situation: If a test 𝗱𝗼𝗲𝘀𝗻'𝘁 𝗿𝘂𝗻 𝗶𝗻 𝗖𝗜, it will break silently sooner or later. Always. AI is accelerating how we write code. But the fundamentals of 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲 remain exactly the same. Root cause. Reproducibility. Automated validation. Everything else is just faster guessing. #SoftwareEngineering #AI #DevOps #EmbeddedSystems #CI/CD

🛠️ Debugging is the New Superpower in the AI Era "AI can write code faster than you. But can it debug production at 2 AM?" 😵💫 Let’s be real 👇 Tools like ChatGPT, Copilot, Claude are revolutionizing the way we write code. They’re fast. Smart. And evolving. But here’s what they can’t do (yet): 🚨 Decode cryptic error logs in real-world systems 🧠 Think clearly under pressure when prod is down 🔍 Trace weird bugs across multiple micro-services 🔗 Understand your business logic & edge cases 💬 Communicate chaos in a calm, structured way (To leadership!) This is where YOU come in. The developers who thrive alongside AI are not just code writers… They're problem solvers. They’re the ones who: ✅ Stay calm in chaos ✅ Ask sharp questions when systems break ✅ See beyond the surface-level errors ✅ Become the go-to person when no one else knows what’s going on AI gives you coding speed. But debugging? That’s still heavily dependent on humans 💡 So before you obsess over writing 100 lines in 10 seconds... Level up your ability to find and fix what breaks. Because in the AI era, the real leaders are the ones who can debug under pressure. 💬 What’s the hardest bug you’ve ever cracked? Drop your “2 AM war story” below 👇 #Debugging #AIvsHuman #TechCareers #SoftwareEngineering #DevLife #ProblemSolving #AIAndYou #DeveloperMindset

Back in my days as a junior engineer, there was a Senior Staff Engineer on my team who worked 40-45 hours a week and yet only pushed a few dozen commits per half.  At first, I didn’t understand it.  I was grinding away, writing code daily, and fixing bugs, while this engineer barely seemed to contribute.  But over time, I realized something game-changing about how high-impact engineers actually work.  So, what did they do?  🔹 They found the biggest inefficiencies and fixed them.   - Instead of churning out new features, they dug into legacy systems, identified slow code paths, and removed bottlenecks that had existed for years.  🔹 They focused on debugging at scale.   - They didn’t just fix bugs. They found the bugs that mattered, the ones costing the company millions in inefficiencies, latency, and downtime.  🔹 They were masters of system-level thinking.   - They understood that a 0.2-second reduction in a critical API response time could lead to millions in revenue gains and a better user experience.  🔹 They made existing systems more efficient.   - One fix they made? A small caching optimization that reduced database queries by 30%, cutting infrastructure costs dramatically.  The Real Lesson?  +  Impact > Lines of Code   - Writing 10,000 lines of new code isn’t as valuable as removing 1,000 lines of bad code that slow everything down.  +  Find the real problems   - The most impactful engineers aren’t just feature builders—they investigate, debug, and solve system-wide inefficiencies.  +  Optimizations drive massive value   - Small, strategic fixes at scale can save millions in infrastructure costs or increase revenue without adding new features.  ► What junior engineers can learn from this?  🔸 Stop thinking that impact = writing more code.   🔸 Learn how to debug and optimize instead of just building new things.   🔸 Look beyond your task list, find inefficiencies, improve what already exists, and focus on long-term system health.  Because the best engineers aren’t the ones who ship the most code.  They’re the ones who make the entire system better. – P.S: If you're preparing for a SWE role, do check out my guide on behavioral interviews. If you want to break into big tech, startups, or MAANG companies, you must ace the behavioral round. This guide will help you do it → https://lnkd.in/drnsTNhU (210 engineers are already using this!)

Debugging... The part of software engineering that nobody really warns you about. One minute you're feeling like a coding genius, the next - you’re in a staring contest with a stubborn error message. But over time, I’ve built a debugging process that helps me stay (mostly) sane: 1. Reproduce it. First step - make it happen again. Consistently. If I can’t reproduce the bug, it’s like chasing a ghost. 👻 2. Break it down. What’s working? What’s not? Narrow it down, line by line if needed, to the smallest piece of code that causes the issue. 3. Google like a pro. Yes, even experienced engineers google errors. The key? Be specific. Error message + tech stack + relevant keywords = faster answers. 4. Rubber duck it. 🦆 No rubber duck? No problem. Explain your problem out loud or write it down. You’d be amazed how often the solution pops up mid-explanation. 5. Ask for help. Stuck for hours? There’s no shame in reaching out. Sometimes a fresh set of eyes finds the answer in minutes. And when it’s finally fixed? Commit that code, celebrate, and add a note for your future self - because yes, bugs have a way of making encore appearances! 😂 What’s your go-to debugging trick? Share in the comments!

🚨 AI Can Code, But Can It Debug? Not Quite Yet. While headlines scream that AI is writing a quarter of Google’s code or powering Meta’s dev workflows, a recent Microsoft Research study throws a dose of reality into the mix—especially for developers who know the true battle isn’t writing code, but debugging it. 🔍 The Study: Microsoft tested 9 top AI models (including OpenAI’s and Anthropic’s) on a debugging benchmark—SWE-bench Lite—to see how well they handle real-world software bugs. Even with debugging tools, performance was underwhelming: Following are the models and their success rate Claude 3.7 Sonnet 48.4% OpenAI’s o1 30.2% OpenAI’s o3-mini 22.1% That means even the best AI model failed more than half of its debugging tasks. 🧠 Why AI Struggles With Bugs: The root problem? Lack of data that reflects how human developers debug—step by step, testing hypotheses, and using tools like pdb, breakpoints, and logs. AI models may be trained on billions of lines of code, but not on the process of debugging. This makes AI assistants great at scaffolding features, but not reliable yet in code triage, especially when stakes are high—like in crypto and blockchain where bugs = breaches. 👩💻 What This Means for Developers: You’re not being replaced. Debugging is still a very human skill. AI still needs your guidance. It’s a tool, not a teammate (yet). Training matters. Better AI will come from better, process-rich datasets—not just more code dumps. 🛠️ As devs, we should stay excited about what AI can offer—but remain critical, especially when debugging, testing, and securing production systems. Let’s build smarter tools, not just flashier ones. Report Link is in the comments 🔁 Agree that debugging is the real test of a coder? Seen AI fail in surprising ways? Drop your experiences below. 👇 #AI #Developers #Debugging #CryptoSecurity #SoftwareEngineering #MicrosoftResearch #Claude #OpenAI #AIinDevTools

Software engineering isn’t just about writing code. It’s about staying sane when things break — and they will break. Here’s the real breakdown nobody talks about: 💻 10% Writing actual code ✅ 90% Debugging, reading logs, staring at the screen wondering “but why?!” 🧠 And somewhere in between, you: Google a 12-year-old StackOverflow post Try turning it off and on again Question your life choices Finally fix it… and don’t touch anything else because it works now But here’s what makes it beautiful: ✅ You learn to think in systems ✅ You get better at solving real problems ✅ You build something from absolutely nothing Every bug you squash and every system you scale makes you a sharper thinker and a stronger problem solver. So next time you’re stuck, frustrated, or buried in logs — remember: you’re becoming a better engineer through it all. I recently started a series where I explore stories from engineers, creators, and people doing incredible things across career paths. It’s called BanZara – and you’ll probably find something that hits home: 🎥 https://lnkd.in/gV9MQhEN Let’s keep building. 🚀 Upasana Singh 👏 .