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If an AI coding assistant says a bug is fixed, treat that as a claim—not a result. A repair is verified only when you can reproduce the original failure, see a relevant test pass after the change, and confirm that the code and dependencies meet the project’s requirements. An assistant can make a plausible change, miss the actual cause, and still confidently report success. The title describes a common failure pattern, not a verified incident tied to a particular model or repository.
Why does an AI coding assistant say a bug is fixed when it is not?
A coding assistant generates and evaluates text based on the context it has, but its confident wording does not establish that it executed the code, reproduced your symptom, or checked the result. It may change a nearby line rather than the cause, misunderstand the failure, overlook an edge case, or report success based on an incomplete test. Sometimes the assistant did not run anything at all; “fixed” is then a prediction about the proposed edit, not a report of observed behavior.
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Even when a tool can run tests, a passing result answers only the question those tests asked. A suite that does not exercise the reported bug can pass while the bug remains. A test may also fail to cover a related path that the edit broke. To judge a repair, you need evidence that connects the original symptom to the change and checks what could have regressed.
This distinction matters especially when the assistant speaks in the past tense. “I fixed it” can sound like a completed operation, but only a reproducible result—such as a failing test before the change and the same test passing afterward—shows that the reported failure changed under known conditions. If the assistant says it ran a test, inspect the actual command and output rather than relying on its summary.
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Can an AI verify its own code?
It can help check its work, but asking the same model to verify a proposed fix is not independent verification. The model may miss a problem in its own change, misunderstand test output, or contradict an earlier answer. A second confident statement from the same assistant is not a second test.
A 2024 study in IEEE Transactions on Software Engineering, “Fight Fire With Fire: How Much Can We Trust ChatGPT on Source Code-Related Tasks?”, evaluated code generation, code completion, and program repair. It reports: “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.” The study found that the model’s self-assessment could be wrong even when it was asked to judge whether its own output was correct.
In that study, average success was 57% for generated code, 73% for code completions without vulnerabilities, and 70% for program repairs. Those are results for the study’s evaluated tasks, not a promise about any one assistant, repository, or current coding tool. The authors also reported average error rates of 39% when judging generated-code correctness, 25% for vulnerability judgments, and 28% for repair-success judgments. These figures are a reason to demand external evidence—not a way to calculate the odds that your particular patch works.
The study tested direct questions, guiding questions, and prompts that supplied test reports. Guiding questions and test-report prompts improved detection in some settings, but missed failures and inaccurate explanations remained. Asking “Are you sure?” or asking the assistant to review its own diff can surface issues worth investigating. Neither changes who performed the verification: it is still the same model reasoning over available context.
How can I tell whether an AI-generated fix actually worked?
Use three checks in order: reproduce the bug, run a targeted regression test, and inspect the diff and its dependencies. This sequence ties the change to an observable failure, then checks both the behavior and the implementation.
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1. Reproduce the reported failure
Before accepting a proposed change, write down what you did, what you expected, and what actually happened. Use the same inputs, environment, and steps that triggered the bug, as far as you can establish them. If the failure is intermittent, record how often it occurs and under what conditions instead of treating one successful run as proof that it is gone.
When possible, make a minimal reproduction: the smallest input or sequence that still demonstrates the problem. A concrete reproduction helps distinguish the original defect from a related symptom and gives you something to rerun after the change. If you cannot reproduce the issue, you may still review a proposed fix, but you cannot claim that the change made that specific failure disappear based on a before-and-after observation.
2. Run a test that would have caught the bug
Choose or add a test that exercises the failing behavior, including the relevant input or condition. Run it against the unchanged code first if practical: it should fail for the reason you are trying to fix. Then apply the repair and run the same test again. A test that passes only after the change is stronger evidence than a general “tests pass” message because it directly covers the reported symptom.
After the targeted test, run the project’s relevant existing test suite. Use the commands documented by the project and keep the exact commands and outputs. If the test runner reports skipped tests, setup errors, or a partial run, note that: a command that did not execute the relevant checks cannot verify them. A passing suite is useful, but its scope is bounded by the cases it actually ran.
Do not let a green result erase a mismatch in requirements. If the expected behavior is not captured by a test, compare the change with the written requirement and the reproduction steps. A test may encode an assumption that is itself wrong, or check only the common case while the defect occurs on an edge case.
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3. Inspect the patch and its assumptions
Read the diff, not just the assistant’s explanation. Check which files changed, whether the edit addresses the root cause, and whether it introduces unrelated modifications. Look for removed checks, weakened validation, broad exception handling, hard-coded values, or changes to behavior beyond the stated requirement. A test pass does not tell you whether the implementation is maintainable, safe, or appropriately narrow.
Check every new or unfamiliar API, method, class, package, and library name. An assistant can suggest something that sounds consistent with a framework but does not exist, or select an API with different behavior than expected. Confirm unfamiliar items in authoritative documentation and verify dependencies in the project’s package registry and lockfile before running or shipping them. A successful-looking import or explanation is not evidence that a dependency is real or appropriate.
Why might the assistant give three different fixes?
Different answers can follow from a changed prompt, newly supplied context, a different proposed explanation of the failure, or the model exploring multiple plausible repairs. The answers may each target a symptom rather than the cause. If one suggestion changes the implementation and another changes the test or dependency, they may also be making incompatible assumptions.
Do not choose among several fixes by confidence or fluency. Compare each patch against the reproduction and the requirement. Ask what observable result would distinguish the proposed causes, then run the smallest safe check that answers that question. If a fix depends on an assumption—such as a particular input shape or library version—verify that assumption in the codebase or authoritative documentation before applying it.
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How common are incorrect fixes and invented dependencies?
Errors are measurable in controlled evaluations, but no single rate predicts the outcome for your code. The 2024 IEEE study’s success and self-judgment figures show that code generation, repair, and self-verification are fallible under evaluated conditions. They do not identify the chance that a named tool will fail on a particular task, and they should not be read as a current benchmark for every model or programming language.
Dependency hallucination is another concrete risk. A BSI/ANSSI report, citing an underlying study, says 19.7% of imported packages were hallucinated across 576,000 generated Python and JavaScript examples. That percentage is scoped to the examples in that study; it does not mean that 19.7% of packages suggested in every project are fake. The practical implication is to check unfamiliar package names rather than adding them on trust.
For code completion, a 2025 paper in ACM Transactions on Computer-Human Interaction by Vasconcelos, Bansal, Fourney, Liao, and Vaughan states that AI code-completion tools “are not always accurate and can introduce bugs or even security vulnerabilities into code if not properly detected and corrected by a human programmer.” This is why human review belongs in the present workflow. NIST’s 2025 review likewise places human–LLM collaboration at the center of current bug-fixing practice and describes autonomous program repair as a research direction, rather than a substitute that removes the need to validate a change.
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Ask for details that make the proposed repair reviewable, then verify those details yourself. A useful request is not “Are you sure?” but a request for the changed files, the suspected cause, the exact tests run, and the commands and outputs. If the assistant cannot run code in the environment, it should say so; if it can, its report still needs to be checked against the actual output.
- Reproduction: What steps or input triggered the original failure, and what result was observed before the change?
- Test coverage: Which test exercises that behavior, and did it fail before the fix and pass after it?
- Execution record: What exact command ran, and did it complete successfully rather than skip or partially execute?
- Patch scope: Which files changed, and how does each change satisfy the requirement?
- Dependency validation: Are new names and APIs present in authoritative documentation and in the project’s declared dependencies?
- Uncertainty: What behavior, environment, or edge case was not checked?
These questions can improve the quality of the assistant’s explanation and help uncover missing evidence. They do not turn its answer into an independent audit. Treat the response as a checklist of claims to verify, not as proof that verification happened.
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What should I do if the bug still appears after the assistant says it is fixed?
Return to the original reproduction rather than adding another patch immediately. Confirm that you are running the code and environment you think you are, and that the latest change is present. Then compare the actual result with the expected behavior and the test result. A persistent symptom may mean the proposed cause was wrong, the tested path differs from the failing path, or the relevant change was not exercised.
- Record the exact failing input or steps and the observed output.
- Check the final diff and confirm that the intended files and lines changed.
- Rerun the targeted test and capture its full result, including setup failures or skipped cases.
- Check related requirements and paths that could have changed as a side effect.
- Reassess the suspected cause before accepting another suggested repair.
If a regression appeared after the patch, identify whether it is caused by the change by comparing behavior with and without that patch under the same conditions, when it is safe to do so. Keep the evidence from each attempt. Repeating a confident prompt without changing the evidence available to the assistant can produce another plausible answer without resolving the uncertainty.
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When is it reasonable to accept “fixed”?
Accept the repair when the relevant failure is reproducible before the change, the targeted check passes afterward, the broader relevant tests do not reveal regressions, and the diff satisfies the actual requirement. Confirm new dependencies and APIs, and make sure the test results correspond to the code you intend to keep. If one of those checks could not be done, describe the remaining uncertainty accurately instead of treating the assistant’s wording as a substitute.
AI assistants can help explain a failure, propose a patch, draft tests, and identify places to inspect. The developer’s task is to connect those suggestions to observed behavior and project requirements. That division of work is not a temporary inconvenience: current guidance and evaluations recognize both the usefulness of assistance and the need for human judgment in checking generated changes.
Frequently Asked Questions
Can ChatGPT run tests on my computer by itself?
Only if the specific product and setup provide it access to an execution environment and your project; a chat response alone does not establish that anything ran. Check the tool’s actual execution transcript or run the project’s test command yourself.
Should I ask a second AI to review a generated fix?
A different model may catch issues the first missed, but it is still another review opinion rather than a behavioral test. Use additional reviews to generate questions for the diff and test plan, not to replace execution against the bug.
What if there is no test for the bug I am fixing?
Create a focused regression test that captures the observed failure and expected behavior, if the project’s test setup allows it. If automated coverage is impractical, preserve a repeatable manual reproduction and document exactly what was checked.
Can passing tests prove that a fix is secure?
A passing functional test suite alone does not establish security; it only reports outcomes for the cases it executes. Review the changed code and dependencies for security-relevant behavior, and use the project’s security checks where available.
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