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AI for Debugging and Code Review

What this concept is​

In assisted debugging, a model proposes explanations or changes; evidence decides whether they are useful. In review, the goal is to find a concrete mismatch with a contract, not to produce an impressive list of generic improvements.

Why it matters here​

You can gain a second perspective on code while retaining responsibility for the change and its consequences. Small, testable suggestions are easier to assess than broad rewrites.

Concrete example​

A suggestion replaces the list of session durations with a set “for efficiency.” For [25, 25], the intended qualifying count is 2; a set produces 1. You can reject the suggestion using a two-item counterexample without debating how confident the model sounds.

Another suggestion changes >= to > while retaining a claim that the threshold is inclusive. [25] distinguishes the two. A code review should name the exact condition, a failing case and the practical consequence.

Common confusion / misconception​

Running a generated test suite does not prove its expected answers are right. The same assistant can repeat the same misconception in both implementation and tests. A code snippet from an unknown source may also have actions unrelated to the task; inspect it before running.

How to use it​

Preserve the original failing case. Ask for competing hypotheses and one discriminating experiment. Inspect the proposed diff, run independently specified tests and review unexpected file changes. Reject changes outside the scope until they have a reason and a verification plan. Record accepted and rejected suggestions in the existing assistance log.

Check yourself​

Can an assistant's “all tests pass” statement substitute for an actual run? No. Require the command, output and environment if it claims execution, then reproduce relevant checks yourself where possible.

Mini drill or application​

Review the three authored policies in the lab. Before running any evaluator, predict which boundary or duplicate case distinguishes them. Explain one rejected suggestion as a concise review comment.

Read this only if stuck​

Anthropic: Building effective agents provides engineering context for simple workflows and tools. It is vendor experience, not independent evidence of educational benefit; its older tooling details are not prescribed here.