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If an AI coding assistant says a bug is “fixed,” treat that sentence as a claim, not a test result. The repair is trustworthy only when you can reproduce the original failure, run a regression test that would fail without the change, inspect the diff against the requirement, and verify every new dependency or API. A model can produce a plausible patch, misread a failing test, and confidently declare success—then generate a different “fix” when asked again.
This pattern is not evidence about one vendor or repository. It is a documented limitation of AI-assisted programming. Your goal is to replace confidence words with observable, repeatable evidence.
Why does my AI coding assistant keep saying the bug is fixed when it is not?
An assistant predicts text that looks like a solution; it does not automatically establish that the program now satisfies your requirement. It may infer that a familiar-looking edit should work, stop after producing a patch, or interpret an incomplete test result as confirmation. “Fixed” is therefore an assertion about the code, not proof about the running system.
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- The prompt describes a symptom but omits an exact reproduction, expected output, or an important environment detail.
- The model recognizes a common bug pattern and applies a standard-looking change that does not match your code path.
- A test command runs only a subset of the suite, skips the failing case, or returns an ambiguous result that the assistant summarizes incorrectly.
- The patch fixes one path while leaving a related path, data shape, race, permission, or validation rule untouched.
- The assistant invents a method, class, package, or option whose name sounds reasonable but is not part of the API you use.
Repeated wording does not increase confidence. Three identical “fixed” messages are still one unverified claim unless each is backed by independent observations.
Can ChatGPT or another AI verify its own code?
It can perform useful self-checks, but self-verification is not independent verification. The same model that proposed a patch also chooses what to inspect, which assumptions to make, and how to interpret the result. It can miss the same flaw in both the code and its explanation, or contradict an earlier answer when the conversation changes.
A 2024 study in IEEE Transactions on Software Engineering tested code generation, code completion, and program repair with direct questions, guiding questions, and test-report prompts. Prompting the model to reason about tests improved detection in some settings, but substantial failures and inaccurate explanations remained. Asking “Are you sure?” can prompt another look; it cannot substitute for running the program and checking the result yourself.
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“ChatGPT often erroneously predicts its generated incorrect code as correct, its vulnerable completed code as non-vulnerable, and its failed program repairs as successful during its self-verification.” — authors of Fight Fire With Fire: How Much Can We Trust ChatGPT on Source Code-Related Tasks?, IEEE Transactions on Software Engineering, 2024.
The practical boundary is simple: an assistant may suggest a test or explain an output, but an independently executed test, a reviewed diff, and a confirmed requirement are the evidence.
What do the measured error rates say about AI repair claims?
Controlled evaluations show that these mistakes are measurable rather than merely anecdotal. The 2024 IEEE study reported the following averages for its evaluated tasks:
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| Measure | Reported average | What it means |
|---|---|---|
| Generated-code success | 57% | About 57% of generated-code tasks succeeded under the study’s conditions; the remainder did not. |
| Code completions without vulnerabilities | 73% | About 73% of completions were judged free of vulnerabilities, so a substantial minority were not. |
| Program-repair success | 70% | About 70% of repair tasks succeeded in the evaluation, leaving many failed repairs. |
| Error judging generated-code correctness | 39% | The model incorrectly judged correctness in 39% of the evaluated judgments. |
| Error judging vulnerability status | 25% | It misjudged whether code was vulnerable in 25% of the evaluated judgments. |
| Error judging repair success | 28% | It incorrectly judged whether a repair worked in 28% of the evaluated judgments. |
These are averages from controlled tasks, not a probability that your particular patch will fail. They do show why a confident completion cannot be treated as a pass/fail oracle. An assistant can be right often enough to feel reliable while still missing a failure that matters.
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How do I know an AI-generated fix actually worked?
Use a three-part check: reproduce the original bug, run a targeted regression test, and inspect the complete change—including dependencies—against the requirement. Do these checks in an environment you control, and save the commands and outputs so someone else can repeat them.
1. Reproduce the failure before accepting a patch
Write down the smallest reliable reproduction: input or request, setup state, command, observed result, and expected result. Run it on the unmodified revision and keep the output. If you cannot make the bug fail, you cannot demonstrate that the patch made it pass; investigate whether you are using the wrong branch, fixture, configuration, or data.
Keep the reproduction deterministic where possible. Pin the relevant runtime and database versions, use a fixed test fixture, and record environment variables that affect behavior. A one-time manual success is weaker than a command another developer can execute twice and obtain the same result.
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Turn the reproduction into an automated test that expresses the requirement, not the implementation detail. First run it against the old revision to confirm it fails for the reported reason. Then apply the patch and run the same test; it should pass. Include nearby cases that could regress, such as empty input, boundary values, authorization failures, retries, and concurrent requests when those conditions are relevant.
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Run the focused test and then the broader suite. A green command is meaningful only when you know which tests executed, whether any were skipped, and whether warnings or setup errors were hidden. Save the exact command and observed output in the change record.
3. Inspect the diff and map it to the requirement
Read the diff line by line instead of relying on the assistant’s summary. Confirm that the changed code is reachable from the failing path, that error handling remains correct, and that the patch does not weaken validation, authentication, transactions, or resource cleanup. Check deletions and configuration changes as carefully as additions.
Compare the implementation with the written requirement and acceptance criteria. If the requirement says “reject,” verify the status, message, and side effects—not merely that an exception was raised. Ask a teammate who did not write the patch to review the behavior and the test; a second human perspective is more independent than asking the same model to re-evaluate itself.
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How should I check dependencies, APIs, and packages an assistant suggests?
Verify every unfamiliar name before allowing it into a build or deployment. Assistants can hallucinate methods, classes, packages, libraries, and version combinations that look authentic but do not exist or do something different from the description.
- Search the authoritative API or package documentation for the exact symbol and version.
- Confirm that the package exists in the registry used by your project and that its published maintainers and release history are plausible.
- Check the project’s manifest and lockfile after installation. Review the resolved version, transitive dependencies, integrity hashes, and licensing policy.
- Read the API’s documented behavior and failure modes; do not infer semantics from a method name.
- Run the smallest test that exercises the new call, including an invalid-input or error-path case.
A BSI/ANSSI report, accessed in 2026, cites a study of 576,000 generated Python and JavaScript examples in which 19.7% of imported packages were hallucinated. That figure does not mean every suggestion is fake, but it makes “the import worked in the answer” an unsafe assumption. A package that happens to exist can still be malicious, abandoned, incompatible with your license, or unrelated to the intended API, so registry and documentation checks remain necessary.
Why did the same assistant give me three different fixes?
Different answers are expected when the model has incomplete context or when generation settings allow variation. It may focus on a different suspected cause, notice a new detail in your follow-up, or produce another statistically plausible completion without learning whether the first one worked.
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Three alternatives are useful as hypotheses, not as a vote. Compare them against the reproduction and requirement:
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- Reject a proposal that adds an unverified dependency merely to avoid understanding existing code.
- Demand an explanation tied to specific lines, inputs, and observed outputs rather than general claims about “handling the edge case.”
- Keep rejected attempts in a temporary branch or patch file so you can identify which change altered the behavior.
If the assistant cannot explain why a test failed before the patch and passed afterward, treat the disagreement as a signal to gather better evidence, not as a reason to select the most confident answer.
Which AI-assisted debugging workflow is safest?
A workflow becomes safer as it adds independent execution, visible changes, requirement coverage, and explicit uncertainty. The following comparison shows what each level can and cannot establish.
| Workflow | Independent test execution | Reproducible commands | Diff visibility | Requirement and dependency checks | What the result proves |
|---|---|---|---|---|---|
| Accept the assistant’s “fixed” message | No | Not established | Not established | Not established | Only that the model asserted success |
| Ask the assistant to reason about its patch | Only if you run the tests yourself | Suggested, not demonstrated | Partial | May be incomplete | A second explanation from the same source |
| Run a focused regression test | Yes, if you execute it | Yes, when recorded | Still requires review | Focused coverage only | That this tested scenario passes in that environment |
| Review diff, dependencies, targeted test, and full suite | Yes | Yes | Yes | Explicitly checked | Strong evidence for the covered requirements, with stated limits |
| Fully autonomous repair | Depends on the system | Must be demonstrated | Must be reviewable | Must be validated | An experimental research direction unless your controls establish otherwise |
Use the last completed workflow as your acceptance standard, not the assistant’s confidence level. If you cannot run the full suite, document exactly which tests and requirements remain unverified.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should an AI assistant report instead of simply saying “fixed”?
A useful report separates edits from evidence. Ask for the files and lines changed, the suspected root cause, the exact commands to run, expected outputs, tests not covered, and any assumptions about versions or data. Require it to label tests it actually executed versus tests it merely recommends.
Prompts that request a test plan, a failure analysis, and a review checklist can improve detection in some situations, as the IEEE evaluation found. They still do not make the model an independent judge. The assistant should state uncertainty when it lacks repository access, cannot execute a command, or has not verified an API.
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A practical verification prompt
You can ask: “Do not claim this is fixed. State the original failing command, the expected and observed results, the exact diff, a regression test that fails on the old revision, commands I must run, dependencies or APIs that need documentation checks, and conditions you have not verified.” This changes the conversation from a binary verdict to an auditable checklist; you still perform the checks.
What are the common failure modes after an AI claims success?
When the symptom persists, classify the failure before requesting another patch. The category determines the next useful action.
- Wrong reproduction: The test uses different configuration, data, branch, or runtime. Re-run the recorded reproduction and compare environments.
- Untested path: The new test covers a happy path but not the failing input, permission, timeout, or concurrency condition. Add the missing case and prove it fails before the patch.
- Partial fix: One layer changes while a cache, serializer, client, migration, or background worker retains the old behavior. Trace the request or data through every layer.
- Regression: The original test passes but another requirement fails. Inspect the full suite and review side effects in the diff.
- Fabricated API: An import or method is nonexistent or has different semantics. Check authoritative documentation and the lockfile, then replace or remove it.
- Misread output: The command returned warnings, skipped tests, or a nonzero status that was summarized as success. Examine the raw exit code and complete log.
Stop iterating on patches until you have a failing, deterministic test. Otherwise each new answer can obscure the original cause and make the eventual fix harder to review.
How can a team make “fixed” an evidence-based status?
Define “fixed” in the contribution checklist: reproduction captured, regression test red before the patch, green afterward, relevant suite run, diff reviewed, dependencies validated, and limitations recorded. Continuous integration can enforce the commands, while a reviewer checks whether the tests represent the requirement rather than merely increasing coverage numbers.
Store the test command, commit identifier, runtime versions, and output with the change. For security-sensitive repairs, add threat-focused review and scanning instead of assuming that a passing functional test proves safety. This process keeps AI useful for generating hypotheses while humans and automated systems provide the independent observations needed for acceptance.
Frequently Asked Questions
Can an AI coding assistant run tests for me?
Some assistants can invoke tools in an attached development environment, but tool access varies and a reported command may be incomplete or misinterpreted. Check the actual terminal log, exit status, repository revision, and test count yourself; text that merely describes a run is not an execution record.
What if the regression test passes but users still see the bug?
The test may not model production data, timing, configuration, or a second code path. Capture a production-like failing case without exposing sensitive data, compare runtime versions and feature flags, and add an end-to-end test that crosses the boundary where the symptom occurs.
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Should I accept a larger AI rewrite if it makes the tests green?
Not automatically. A broad rewrite increases the number of behaviors that need review and can make a green, narrow test suite misleading; request a smaller change or add tests for every altered requirement before approval.
How do I record an AI-assisted fix for an audit?
Keep the original failure reproduction, the assistant’s proposed diff, the final reviewed diff, dependency and API verification notes, exact test commands, raw outputs, and reviewer identity. Record what was not tested so later readers do not mistake limited evidence for a universal guarantee.
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