AI to Fix Code: Choose a Tool and Verify the Repair
AI coding assistants can propose fixes for bugs, but the quality of the repair depends on how well you describe the failure. Here is how to supply the right evidence and verify the result.
Updated 5 min read
AI coding assistants can propose fixes for bugs quickly, but the quality of the repair depends on what you give them. An assistant that sees a vague description will often produce a plausible-looking fix that addresses a symptom rather than the cause. Give it a reproducible failure, the exact error message and the code where it happens, and you get a much better result.
Tools like fenbs help by preserving the bug context, accepted fixes and lessons from past debugging sessions, so the next AI session starts with what worked before rather than repeating the same attempts.
What makes a good bug description for AI
A useful bug description has four parts: what you expected to happen, what actually happened, the exact error message or incorrect output, and the smallest piece of code that shows the problem. The last part is the hardest to write and the most important.
- Expected behavior: what the code should do in plain language.
- Actual behavior: what it does instead, including any error message in full.
- Reproduction steps: the minimum inputs and conditions that trigger the failure.
- Relevant code: only the function or block where the failure occurs, not the whole file.
If you cannot write a minimal reproduction, the bug is not yet well enough understood to fix. Ask the AI to help you identify the minimum case first. That process often points directly at the cause.
Comparing AI debugging tools
| Tool | How it helps with bugs | Best fit | What to check |
|---|---|---|---|
| Claude (claude.ai or API) | Strong at reasoning through complex logic errors and explaining root causes; can suggest multiple repair approaches. | Logic bugs, design issues, understanding error messages. | Paste the exact error and the relevant function; avoid pasting files larger than the context allows. |
| ChatGPT | Broad knowledge of common error patterns; good at explaining stack traces in plain language. | Common library errors, quick explanations of error messages. | Use the latest model for code tasks; older models miss recent language and library changes. |
| GitHub Copilot | Inline fix suggestions in the editor; sees the surrounding code without a paste. | Inline fixes while writing; quick completions for obvious errors. | Works best when the error is visible in the open file; less useful for errors across multiple files. |
| Cursor agent mode | Can read the whole repository, reproduce errors and apply fixes across multiple files. | Multi-file bugs where the cause is not obvious from one function. | Define the bug clearly before starting agent mode; review the diff before applying. |
| fenbs | Not a code editor. Keeps the bug task, reproduction steps, accepted fixes and lessons for future sessions. | Preserving bug context across sessions and team members. | Needs an AI that supports MCP to read the stored context; see Integrations for supported tools. |
No AI tool can guarantee a correct fix. Each proposal needs a human to verify that the fix addresses the root cause and does not introduce a new problem. Run your tests after every change.
Preserve bug context across sessions
When a bug spans multiple debugging sessions or involves more than one person, the context easily gets lost in chat history. In fenbs, add a task for the bug with the reproduction steps, the error message and what has already been tried. The next AI session reads that context before proposing a fix.
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How to verify the fix
Verifying a fix means more than checking that the error message disappears. Check three things: the original failure no longer occurs, no other test is now failing, and the fix makes sense in terms of the root cause you identified.
- Run the reproduction case you wrote before the fix. Confirm the error is gone.
- Run the full test suite. A fix that introduces a regression in a passing test is not a complete fix.
- Read the change the AI made and confirm you understand it. If you do not understand it, ask the AI to explain it before you commit.
- Write a test for the fixed behavior so the same regression cannot be introduced later without a failing test.
- Record what the root cause was and what the fix did in the task note.
When the fix makes things worse
AI-proposed fixes occasionally introduce new failures. This is more likely when the AI does not have full context: it fixes what it sees and misses a dependency. When a fix breaks something new, revert the change, add more context to the bug description and try again. Do not apply the fix to the broken result.
Reviewing AI-generated code is a learnable skill. The reviewing AI-generated code guide covers what to look for when an AI proposes a change. A fix that passes tests but contains an unrelated cleanup or style change should still be reviewed before committing.
Keeping a record for future sessions
The most useful thing to record after fixing a bug is not the fix itself, which is in the code, but the root cause and what made the fix correct. A note like "the off-by-one error was in the loop upper bound; fixed by using array.length instead of array.length - 1" tells the next session something the code cannot.
When the same class of bug appears more than once, write a Lessons learned entry. An AI that reads "we have had off-by-one errors in every loop over this data structure; check the bounds" will look for that pattern before proposing code that introduces the same mistake.
How to do this with fenbs
- Create a free account and add your project.
- Add a task for each bug, with the reproduction steps and the exact error message in the task note.
- Record what each session tried and whether it worked, so the next session does not repeat failed attempts.
- After a fix is verified, write the root cause in the task note and add a Lessons learned entry if the finding applies to future work.
- Connect your AI over MCP from Integrations and ask it to read the task context before proposing a fix.