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Bug Fixing and Debugging With AI

5 min read
QaTest Auto

Backend

AI is good at 'this error usually means X.' It's bad at 'our codebase does Y so the real cause is Z.' You own the context.

Qa

AI can suggest test cases for a bug. It can't replicate 'user clicked here, then here, then everything broke.' That's you.

Test Auto

Use AI to generate repro steps and test code. Always verify the repro actually triggers the bug.

Bug Fixing and Debugging With AI

TL;DR

  • AI is excellent at interpreting error messages, stack traces, and "why does this code throw?"
  • AI hallucinates when context is missing. It'll confidently suggest fixes for the wrong problem.
  • Best practice: give it the full picture (error, code, what you tried). Treat suggestions as hypotheses, not answers.

AI can feel like a superpower for debugging. It can also send you down rabbit holes. Here's the honest map.

Where AI Helps

Error Message Interpretation

You paste: A Python traceback, a React error boundary, or a cryptic database error.

AI does: Explains what the error usually means. Suggests common causes. Often nails it for standard library / framework errors.

Example: "TypeError: Cannot read property 'map' of undefined" — AI will say "something is undefined when you expect an array" and suggest null checks. Correct 90% of the time for generic cases.

Stack Trace Navigation

You paste: A 50-line Java stack trace.

AI does: Identifies the relevant frames. Explains the call chain. Points to the likely culprit.

Caveat: If the bug is in a library you've forked, or in code AI hasn't seen, it can guess wrong. Always verify the suggested line is actually in the failure path.

"Why Doesn't This Work?"

You paste: A snippet and a description. "This should return X but returns Y."

AI does: Reasons through the logic. Spots off-by-one errors, wrong variable names, async timing issues. Often fast and accurate for contained problems.

Where AI Hallucinates

Missing Context

You paste: "This function returns null sometimes."

AI suggests: Add a null check and return a default.

Reality: The real bug is that the caller is passing wrong inputs, or a dependency is misconfigured. AI fixed a symptom. The root cause is elsewhere. You chase the wrong lead.

Confidence Without Ground Truth

AI will say "the fix is to do X" with high confidence. It doesn't know if X is correct. It's pattern-matching against similar problems. For standard bugs, that works. For weird, domain-specific, or novel bugs — it's a guess.

Incomplete Code

You paste: 20 lines. The bug is in the 50 lines you didn't paste — or in the integration between this and another module.

AI fixes: The 20 lines. The bug persists. You assume AI was wrong. Sometimes the bug was just outside the scope. AI can't see what you didn't show.

How to Use AI for Debugging

  1. Paste the full error. Don't summarize. Copy the stack trace, the logs, the exact message.
  2. Include relevant code. Not the whole repo — but the function, the caller, and anything that sets up the failing state.
  3. Say what you tried. "I already checked for null" saves AI from suggesting it again.
  4. Treat output as a hypothesis. Run the fix. Does it work? If not, iterate. Don't assume the first suggestion is correct.
  5. For production bugs: Include env, config, or "this works locally but fails in prod." Context matters.

Quick Check

You paste 'This function returns null sometimes' and AI suggests adding a null check. What's the risk?

You stare at the stack trace. Google the error. Scroll through docs. Try fixes blindly. Maybe 2 hours later you find it — or you give up and ask a colleague.

Click "With AI" to see the difference →

Do This Next

  1. Debug one real bug with AI. Paste the error and code. Get a suggestion. Verify it. Did it work? What would you have done differently?
  2. Log one AI hallucination. When AI suggests something that's wrong, note it. You'll build intuition for when to trust and when to double-check.