{"slug": "ai-can-write-your-code-can-it-actually-debug-it", "title": "AI Can Write Your Code. Can It Actually Debug It?", "summary": "An engineer discusses the challenges of using AI for debugging code, contrasting it with code generation. The post highlights that AI coding assistants excel at writing code but struggle with investigating failures, which requires tracing execution paths and understanding application state. The author suggests that effective AI debugging should identify root causes and reproduce bugs, not just explain error messages.", "body_md": "AI coding assistants have changed how developers write software.\n\nYou can describe a feature, generate a function, refactor a component, write a test, or explain an unfamiliar codebase in seconds.\n\nBut there is one part of software development that is still surprisingly difficult:\n\n**figuring out why something broke.**\n\nWriting code and investigating a failure are two very different problems.\n\nWhen an application crashes, the answer usually isn't sitting inside the error message.\n\nYou have to reconstruct what happened.\n\nConsider this Node.js error:\n\n```\nTypeError: Cannot read properties of undefined (reading 'email')\n\n    at getUser (/app/services/user.js:42:18)\n    at processRequest (/app/controllers/auth.js:87:12)\n    at async handler (/app/routes/auth.js:31:5)\n```\n\nThe immediate problem appears obvious.\n\nSomething is undefined.\n\nBut what caused it?\n\nMaybe:\n\nThe stack trace tells you **where the program finally failed**.\n\nIt doesn't necessarily tell you **where the bug began**.\n\nThat's the difference between error reporting and debugging investigation.\n\nMost AI coding workflows look something like this:\n\n```\nDeveloper\n   ↓\nPrompt\n   ↓\nAI\n   ↓\nCode\n```\n\nDebugging is different:\n\n```\nFailure\n   ↓\nError\n   ↓\nStack trace\n   ↓\nExecution path\n   ↓\nApplication state\n   ↓\nRoot cause\n   ↓\nFix\n```\n\nThe AI needs to reason across that chain.\n\nSimply asking:\n\n\"What does this error mean?\"\n\nusually produces a list of possible explanations.\n\nThat's useful, but it's not necessarily an investigation.\n\nA better question is:\n\n\"Given this failure and its context, what is the most likely root cause, what evidence supports it, and how can I reproduce it?\"\n\nThat's a much more interesting problem for AI.\n\nImagine this code:\n\n``` js\nasync function getProfile(userId) {\n    const user = await getUser(userId);\n\n    return {\n        name: user.profile.name,\n        email: user.profile.email\n    };\n}\n```\n\nAnd the application crashes here:\n\n```\nuser.profile.email\n```\n\nA quick fix might be:\n\n```\nif (!user) {\n    return null;\n}\n```\n\nBut that might not solve the actual problem.\n\nWhy is `user`\n\nmissing?\n\nMaybe `getUser()`\n\nis querying the wrong database.\n\nMaybe the user ID comes from an expired authentication token.\n\nMaybe a deleted account is still referenced somewhere.\n\nMaybe an API changed its response format.\n\nMaybe the application has a race condition.\n\nThe correct debugging process is to trace the value backward.\n\n```\nuser.profile.email\n        ↑\n      user\n        ↑\n   getUser(userId)\n        ↑\n   authenticated ID\n        ↑\n   incoming request\n```\n\nThe goal is to find the first point where reality differs from what the program assumes.\n\nA good debugging workflow should answer more than:\n\n**What line crashed?**\n\nIt should answer:\n\nIdentify the exact exception and operation.\n\nFind the relevant file, function, and line.\n\nReconstruct the call chain.\n\nInspect the values flowing through the system.\n\nThis is often where the real bug becomes visible.\n\nA reproducible bug is much easier to fix confidently.\n\nThe solution should address the cause rather than simply hiding the exception.\n\nTypeScript prevents many classes of bugs.\n\nBut external data still exists outside the type system.\n\nFor example:\n\n``` js\ninterface User {\n    id: string;\n    email: string;\n}\n\nconst response = await fetch(\"/api/user\");\n\nconst user = await response.json() as User;\n```\n\nThis looks safe.\n\nBut this:\n\n```\nas User\n```\n\ndoesn't validate the actual response.\n\nThe server could return:\n\n```\n{\n    \"id\": \"123\"\n}\n```\n\nand the runtime value still doesn't contain `email`\n\n.\n\nThis is one reason TypeScript applications can still have confusing runtime errors.\n\n**Compile-time types describe what we expect.**\n\nThey don't guarantee that every external system behaves according to those expectations.\n\nModern JavaScript applications are heavily asynchronous.\n\nA single request might pass through:\n\n```\nHTTP request\n    ↓\nMiddleware\n    ↓\nController\n    ↓\nService\n    ↓\nDatabase\n    ↓\nThird-party API\n    ↓\nTransformation\n    ↓\nResponse\n```\n\nThe visible exception could happen at the end of this chain while the original problem happened several steps earlier.\n\nThat's why debugging complex Node.js applications often feels like detective work.\n\nYou're reconstructing a sequence of events from incomplete evidence.\n\nWhen you encounter a difficult Node.js error, don't immediately change random lines of code.\n\nStart with the evidence.\n\nKeep the complete:\n\nAvoid reducing a complex error to something like \"Node is crashing.\"\n\nThe details matter.\n\nIdentify the exact file, function, and line where the exception occurs.\n\nThen inspect the surrounding code.\n\nDetermine which functions called the failing function.\n\nFor example:\n\n```\nAPI handler\n    ↓\nController\n    ↓\nService\n    ↓\nRepository\n    ↓\nDatabase\n```\n\nLook at the values being passed between those functions.\n\nAsk:\n\nWhich value isn't what the code expected?\n\nThe line that crashes is not always where the bug started.\n\nThe most useful question is:\n\nWhere did the application first enter an unexpected state?\n\nTry to identify the exact conditions that trigger the bug.\n\nA reliable reproduction is often more valuable than ten guesses about the cause.\n\nDon't simply suppress the exception.\n\nMake the application correctly handle the state that caused it.\n\nOnce the bug is fixed, make sure it cannot silently return.\n\nThere's a huge difference between these two approaches.\n\n```\nMaybe the database is broken.\n\nTry restarting the server.\nThe database query returns no record for users\ncreated without a profile. The service assumes the\nrecord exists and then accesses profile.email\nwithout validation.\n```\n\nThe second one is actionable.\n\nGood debugging isn't about generating the largest number of possible causes.\n\nIt's about **reducing uncertainty until one explanation is supported by evidence**.\n\nThis is where AI-assisted debugging gets interesting.\n\nInstead of using AI only as a code generator, you can use it as an investigation assistant.\n\nFor example, provide:\n\nThen ask the AI to reason through the evidence.\n\nA useful investigation might produce something like:\n\n```\nError\n-----\nTypeError: Cannot read properties of undefined\n\nLocation\n--------\nuserService.ts:87\n\nLikely root cause\n-----------------\ngetProfile() assumes every user has a profile,\nbut newly created users can exist without one.\n\nEvidence\n--------\nThe profile lookup returns undefined for users\ncreated before profile initialization.\n\nReproduction\n------------\n1. Create a new user.\n2. Skip profile initialization.\n3. Request /profile.\n4. Access profile.email.\n\nRecommended fix\n----------------\nValidate the profile result before accessing\nnested properties and add a regression test.\n```\n\nThat's considerably more useful than:\n\n\"You should check whether profile is undefined.\"\n\nAI code generation asks:\n\n\"What code should I write?\"\n\nAI debugging asks:\n\n\"What happened?\"\n\nAnd then:\n\n\"Why did it happen?\"\n\nAnd finally:\n\n\"What evidence supports the explanation?\"\n\nThis distinction matters.\n\nA coding assistant can generate a plausible fix without necessarily understanding the complete system failure.\n\nA debugging investigation should aim to connect:\n\n**symptom → execution path → state → violated assumption → root cause → fix**\n\nI've been building **KaudDoc**, an AI-powered deep code investigation tool around this problem.\n\nThe idea is simple:\n\n**Don't stop at explaining the error. Investigate the failure.**\n\nInstead of only asking:\n\n\"What does this stack trace mean?\"\n\nthe goal is to get closer to:\n\n\"What actually happened, why did it happen, and what should I investigate next?\"\n\nKaudDoc is designed around debugging investigations involving things such as:\n\nThe goal isn't to replace your IDE debugger.\n\nIt's to provide another layer of investigation when a normal error message or stack trace isn't enough to understand a complicated failure.\n\nA useful investigation shouldn't just produce a wall of AI-generated text.\n\nIt should organize the evidence.\n\nFor example:\n\n```\n┌─────────────────────────────┐\n│ ERROR                       │\n│ TypeError                   │\n└──────────────┬──────────────┘\n               ↓\n┌─────────────────────────────┐\n│ EXECUTION PATH              │\n│ API → Controller → Service  │\n└──────────────┬──────────────┘\n               ↓\n┌─────────────────────────────┐\n│ FAILED ASSUMPTION           │\n│ Profile always exists       │\n└──────────────┬──────────────┘\n               ↓\n┌─────────────────────────────┐\n│ ROOT CAUSE                  │\n│ Missing profile record      │\n└──────────────┬──────────────┘\n               ↓\n┌─────────────────────────────┐\n│ RECOMMENDED FIX             │\n│ Validate profile + test     │\n└─────────────────────────────┘\n```\n\nThe value is not just the final answer.\n\nThe value is the reasoning path that connects the evidence to the conclusion.\n\nLocal debugging is relatively easy.\n\nYou can:\n\nProduction debugging is different.\n\nYou may have:\n\nThis is where structured investigation becomes particularly useful.\n\nA production debugging workflow should help answer:\n\nI think we're going to see a shift in how AI developer tools are built.\n\nThe first generation focused heavily on:\n\n**\"Write this code for me.\"**\n\nThe next generation can go deeper:\n\n**\"Understand this failure and help me investigate what happened.\"**\n\nThose are fundamentally different tasks.\n\nCode generation is primarily about producing an output.\n\nDebugging is about reasoning from evidence.\n\nAnd real-world software failures rarely come with perfect information.\n\nYou might have one stack trace, several logs, a large codebase, and a bug that happens once every few hours.\n\nThat's where an AI system that can investigate rather than simply autocomplete becomes interesting.\n\nThe next time you encounter a difficult JavaScript, TypeScript, or Node.js error, use this checklist:\n\n```\n[ ] Capture the complete error\n[ ] Read the entire stack trace\n[ ] Identify the failing operation\n[ ] Reconstruct the execution path\n[ ] Trace the relevant data\n[ ] Find the first invalid assumption\n[ ] Reproduce the failure\n[ ] Identify the root cause\n[ ] Apply the smallest correct fix\n[ ] Add a regression test\n[ ] Verify the fix\n```\n\nThis process works whether you're debugging a small JavaScript project or a large Node.js application.\n\nThe best debugging question isn't:\n\n\"How do I make this error disappear?\"\n\nIt's:\n\n\"Why did the system get into this state?\"\n\nOnce you can answer that question, the fix usually becomes much clearer.\n\nAI has become remarkably good at helping developers write code.\n\nThe next interesting challenge is making it equally useful when the code doesn't behave the way we expected.\n\n**Debug the symptom. Investigate the system. Find the cause.**\n\nIf you're interested in AI-assisted debugging and deep code investigation, KaudDoc is the project I'm building around this idea.", "url": "https://wpnews.pro/news/ai-can-write-your-code-can-it-actually-debug-it", "canonical_source": "https://dev.to/kauddochq/ai-can-write-your-code-can-it-actually-debug-it-2g7", "published_at": "2026-09-04 03:23:51+00:00", "updated_at": "2026-09-04 03:53:09.714884+00:00", "lang": "en", "topics": ["artificial-intelligence", "developer-tools"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/ai-can-write-your-code-can-it-actually-debug-it", "markdown": "https://wpnews.pro/news/ai-can-write-your-code-can-it-actually-debug-it.md", "text": "https://wpnews.pro/news/ai-can-write-your-code-can-it-actually-debug-it.txt", "jsonld": "https://wpnews.pro/news/ai-can-write-your-code-can-it-actually-debug-it.jsonld"}}