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Debug an AI coding assistant’s suggestion as an untrusted change, not as an explanation. First capture the exact failure and expected behavior, then reduce it to a reproducible case. Inspect the complete diff in the context of your project, ask for a narrow and testable hypothesis, and verify the proposed change with tests, compilation, static analysis, dependency checks, security review, and human judgment. A plausible explanation or green syntax check is not proof that the patch is correct.
Why AI-suggested code needs a stricter debugging process
An assistant can produce code that looks idiomatic and still be wrong in several different ways. It may misunderstand the requirement, infer the wrong data shape, call an API that does not exist in your version, omit an error path, or introduce a security problem while fixing the visible symptom. Compilation proves that a particular set of syntax and type rules were satisfied; it does not prove that the behavior matches the feature you intended.
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The surrounding repository is part of the specification. Requirements, documentation, database constraints, API contracts, error-handling conventions, supported runtime versions, and established patterns all determine whether a technically plausible change is acceptable. Generated code can also make a failure appear to disappear by weakening, skipping, or deleting a test. Treat every suggestion as a hypothesis that must earn acceptance through observed behavior.
Use this evidence-first workflow
The sequence below keeps observation separate from explanation and limits each edit to something you can verify.
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| Stage | Question to answer | Evidence to keep |
|---|---|---|
| Capture | What exactly failed? | Full error, failing test, input, expected result, actual result, and environment |
| Reproduce | Can the failure be triggered reliably? | Smallest input and shortest execution path that still fails |
| Inspect | What did the assistant actually change? | Complete diff and affected project contracts |
| Hypothesize | Which code path could explain the observation? | One or two competing causes tied to specific lines |
| Verify | Does the smallest change fix the behavior? | Regression test, broader tests, build output, lint and static-analysis results |
| Review | Is the change safe and maintainable? | API, dependency, edge-case, security, and human-review findings |
1. Capture the failure before editing
Do not begin by asking the assistant to rewrite the function. Preserve the state that demonstrates the problem. Record the behavior the feature is supposed to provide and contrast it with what actually happened.
Record the observable facts
- Copy the complete error message and stack trace, including the first relevant cause rather than only the final wrapper exception.
- Save the exact test name or command that fails, the input values, and the output you received.
- Write the expected result in a form that can be asserted: a returned value, HTTP status, database state, emitted event, or user-visible action.
- Note the commit or branch, language and runtime versions, operating system or container image, dependency lockfile state, feature flags, and configuration that affect execution.
- Distinguish a deterministic failure from a timeout, race, flaky test, external-service outage, or data-dependent incident.
This record prevents the assistant from filling gaps with assumptions. Give it the error and the relevant code first, and ask it to analyze the evidence without proposing a rewrite. If the assistant cannot explain which observation it is using, its diagnosis is not yet grounded.
Separate symptom from requirement
A message such as “undefined is not a function” describes a symptom, not the intended behavior. State the contract separately: for example, “When an order has no discount, the total should remain unchanged and the response should still be a successful checkout.” That distinction matters because a patch that suppresses the exception may still violate the contract.
2. Make the smallest reproducible failure
Reduce the problem until one input and one execution path still demonstrate it. Start with an existing failing test when one exists. If there is no suitable test, write a focused regression test from the intended behavior before changing production code.
- Identify the shortest command, request, job, or UI action that reaches the faulty code.
- Remove unrelated fields, records, middleware, network calls, and setup while checking that the failure remains.
- Replace nondeterministic data with fixed values. Freeze time, seed randomness, and use a controlled fixture when those factors matter.
- Keep the smallest input that distinguishes the bad result from the expected result. Include boundary values such as an empty collection, zero, maximum length, duplicate identifier, missing permission, or malformed payload when they trigger the issue.
- Turn the reproducer into a regression test whose assertion expresses the requirement, not merely the absence of an exception.
A minimal reproducer gives you a falsifiable experiment. If a proposed fix changes ten unrelated files, you can still run the same narrow test and determine whether the original behavior changed for the right reason.
When the failure will not reproduce
Do not let an assistant invent certainty from an intermittent report. Compare the reporter’s environment with yours, inspect logs and timestamps, and look for concurrency, cache, locale, time-zone, feature-flag, and external-service differences. Add temporary diagnostics or a deterministic test harness. If the issue remains unrepeatable, label the cause unconfirmed and make only reversible observability changes until new evidence appears.
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3. Inspect the complete diff in project context
Read the actual files and changed lines rather than relying on the assistant’s summary. Review the diff from the first changed line to the final generated file, including configuration, migrations, tests, lockfiles, and deleted code.
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- Compare names, data types, error semantics, transaction boundaries, and authorization rules with adjacent implementations.
- Read the relevant requirement and documentation. Ask what assumptions the patch makes about users, business rules, ordering, retries, or eventual consistency.
- Check whether the code follows local patterns for validation, logging, resource cleanup, feature flags, and backwards compatibility.
- Look for changes outside the stated scope: altered defaults, new network calls, broadened permissions, modified schemas, or silently changed response formats.
- Review generated files and lockfiles. A dependency update hidden in a large diff can change runtime behavior even when the edited function looks correct.
Use version control to inspect additions, modifications, renames, and deletions. A clean-looking final file can conceal a removed guard or a test that was weakened. The question is not merely “Does this code work?” but “Does this change belong in this project, with these guarantees?”
4. Ask the assistant for a narrow, testable hypothesis
Once the reproducer and diff are known, ask for analysis constrained by the evidence. Require the assistant to identify the relevant lines, state assumptions, and propose an experiment that could disprove its explanation.
Useful diagnostic prompts
Based only on this error, input, and the attached code, what is the smallest reproducible failure?Give me two plausible causes, the evidence for each in these lines, and one test that would distinguish them.Trace the failing input through this function. Identify the first point where actual behavior diverges from the stated requirement.Suggest the smallest change that addresses the failing test; do not modify, remove, skip, or weaken tests.Which APIs and dependencies in this diff need verification against official documentation?What edge cases, security risks, and project conventions should I review before accepting this change?
Ask for a patch only after the cause has a testable explanation. A confident narrative is not evidence: the assistant may produce a plausible story that does not follow from the code. Compare its claims with execution, documentation, and the project’s own tests.
5. Run checks in increasing scope
Start with the smallest check that can falsify the hypothesis, then expand. GitHub’s institutional guidance states, “Always run automated tests and static analysis tools first.” In practice, run the targeted regression test before the broader suite, but do not stop there.
- Targeted behavior test: Run the new or existing test for the minimal reproducer. Confirm that it fails before the fix when practical, then passes after the change.
- Related tests: Exercise neighboring modules, error paths, integration boundaries, and compatibility cases that share the changed contract.
- Compilation or build: Compile the supported targets and inspect warnings, generated artifacts, and environment-specific failures.
- Static analysis and lint: Run the project’s configured linters, type checker, security rules, and data-flow analysis. Treat warnings as review items, not cosmetic noise.
- Broader suite and CI-equivalent checks: Run the relevant package or repository suite with the same settings used in continuous integration.
- Original reproducer after the final edit: Re-run the exact command, input, and environment that first demonstrated the problem.
If a test fails, determine whether the failure reveals a real regression, an incorrect test assumption, an environment problem, or a defect in the patch. Deleting or skipping the test is not a repair unless the required behavior has intentionally changed and that change has been reviewed.
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Interpret a green result correctly
A passing test proves only the behavior covered by that test under that setup. It does not prove untested branches, production data shapes, concurrency, or security properties. Look for missing assertions, tests that never execute the changed branch, mocks that hide a real interface, and snapshots that accept an unintended output without checking its meaning.
6. Verify APIs, packages, and licenses independently
Generated code often contains familiar-looking but nonexistent methods, parameters from another library version, or a package name chosen from pattern matching. Open the authoritative API documentation for your exact language, framework, and version. Confirm the signature, return values, error behavior, deprecation status, and required initialization.
Audit new dependencies
- Confirm that every package exists in the intended registry and supports your runtime and platform.
- Check maintenance activity, release history, issue responsiveness, ownership, and provenance rather than trusting a convincing README.
- Inspect direct and transitive dependencies, install scripts, native components, and requested permissions.
- Verify the license is compatible with your project and distribution model.
- Pin or lock versions according to project policy and review the resulting lockfile diff.
If documentation and generated code disagree, the documentation and a small local experiment outrank the assistant’s explanation. Remove an unnecessary dependency instead of accepting it merely because it makes the patch shorter.
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After the functional path works, test the boundaries implied by the requirement. Include missing, empty, oversized, duplicated, stale, and malformed data; retries and partial failures; timeouts; concurrent requests; and permission changes during an operation. For parsers and serializers, test unexpected encodings and nesting. For stateful code, test restart and rollback behavior.
Review security-sensitive paths
Inspect input validation, authorization and tenant isolation, secret handling, logging, output encoding, file and network access, deserialization, command construction, and dependency supply-chain risk. Check whether an error message now exposes internal data or whether a convenience fallback bypasses an access check. Automated security scanners and assistant security-review features can identify useful leads, but they complement ordinary security practices and human review; they do not replace them.
Check maintainability
- Is the control flow understandable to a developer who did not generate it?
- Does the patch preserve meaningful names, comments, observability, and error context?
- Does it introduce duplicated logic, hidden global state, unnecessary abstraction, or a brittle special case?
- Can the regression test fail for the original reason and explain the intended behavior to a future maintainer?
For complex or sensitive changes, ask another developer to review the diff. A second person can notice a business-rule mismatch or threat that automated checks and the original author both missed.
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8. Accept only a verified outcome
Acceptance requires more than a successful command. Before merging, confirm all of the following in the final state:
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- The regression test expresses the requirement and was not weakened to obtain a pass.
- Relevant tests, compilation, linting, static analysis, and CI-equivalent checks are complete, with warnings understood.
- Every unfamiliar API and dependency has been checked against authoritative documentation and project policy.
- Edge cases, error paths, authorization, data exposure, and operational impact have been reviewed for the feature’s risk level.
- The final diff contains no unrelated edits, accidental deletions, debug code, secrets, or unexplained configuration changes.
- The implementation fits project conventions and remains understandable without the assistant’s conversation.
If any item is unresolved, keep the change unaccepted and record the open question. For a high-impact patch, use a staged rollout, feature flag, extra telemetry, or a peer review rather than relying on confidence in the generated explanation.
Choose the investigation mode that fits the problem
Different workflows provide different kinds of evidence. None is a universal substitute for the others.
| Approach | Best use | Strength | Typical blind spot |
|---|---|---|---|
| Test-first manual debugging | A known failure with a clear contract | Creates an independent, reproducible oracle before implementation changes | Can be slow when the system boundary or cause is unfamiliar |
| Assistant-led diagnosis | Tracing unfamiliar code and generating competing hypotheses | Surfaces relevant lines, edge cases, and experiments quickly | May confidently infer a cause that execution does not support |
| Automated analysis | Regression detection, type errors, data-flow issues, and repeatable policy checks | Runs consistently across the codebase and in CI | Cannot determine business intent or guarantee coverage of untested behavior |
Combine them when possible: establish an independent test, use the assistant to explore hypotheses, and let automated checks enforce repeatable properties. Evaluate each method by reproducibility, evidence linking cause to code, fit with project intent, interface and dependency verification, security coverage, and the availability of human review.
Common failure patterns and recovery steps
The assistant “fixes” the symptom by swallowing an error
Restore the failing assertion and inspect the caller contract. Decide whether the error should be handled, translated, retried, or surfaced. Add a test for the expected failure semantics instead of accepting an empty result or broad catch block.
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The patch passes the new test but breaks production data
Compare the fixture with real boundary conditions: nullability, legacy records, ordering, size, encoding, and concurrent updates. Add a representative test or migration guard, then run an integration test against a safe copy of the relevant schema and data shape.
The assistant proposes deleting or skipping a failing test
Stop and identify what requirement the test protects. If the requirement changed, update the specification and add a replacement assertion with reviewer approval. If it did not, treat the deletion as evidence that the patch is not correct.
The code compiles, but CI or another platform fails
Compare toolchain versions, operating-system behavior, environment variables, generated files, optional dependencies, and case sensitivity. Reproduce with the CI-equivalent container or command before changing application logic.
The issue disappears after unrelated edits
Revert to the smallest diff and bisect the changes. A broad rewrite can mask the cause, alter timing, or remove the path that exposed the defect. Keep only the change demonstrated to fix the reproducer.
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Prompt templates for a disciplined debugging session
These prompts keep the assistant’s role analytical and make its output easy to challenge:
Here is the expected behavior, exact error, minimal input, environment, and relevant diff. Summarize only the observed facts, then list assumptions you would need to verify.Trace this input line by line. At each step, state the value or state transition you expect and what the code actually does.Propose two causes. For each, cite the relevant line, predict an observable result, and give one experiment that could falsify it.Suggest the smallest production change and a regression test. Do not edit existing tests except to clarify an intentionally changed requirement.Review this final diff for missing functional tests, hallucinated APIs, dependency and license concerns, edge cases, authorization, data exposure, and project-convention violations.
One particularly useful review question is: What functional tests to validate this code change do not exist or are missing? Treat the response as a list of candidates to investigate, not as proof that a gap exists.
When to pause and ask for human review
Escalate when the change affects authentication, authorization, payments, personal data, cryptography, migrations, public APIs, concurrency, production infrastructure, or a large portion of the codebase. Also ask for review when the requirement is ambiguous, the failure cannot be reproduced, dependencies are unfamiliar, tests provide weak coverage, or the assistant’s explanation conflicts with documentation or observed behavior.
A reviewer should receive the original failure, expected behavior, minimal reproducer, final diff, test and analysis results, unresolved assumptions, and any security or dependency findings. That package lets the reviewer evaluate both whether the bug is fixed and whether the fix belongs in the system.
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Use the assistant to generate hypotheses, tests, and candidate edits faster—not to waive verification. The reliable endpoint is an observed fix for a reproducible failure, supported by an independent regression test, broader project checks, verified interfaces and dependencies, edge-case and security review, and a final human reading of the diff. If you cannot show that chain of evidence, the code remains a suggestion.
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