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AI Code Patches: How to Score Changes Before They Reach Git

Treat an AI-generated diff as a proposal. Check that it parses, applies to the current tree, passes an available project command, and touches only authorized files; then review the change itself.
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A fluent explanation is not proof that an AI-generated code change works. Treat its unified diff as a proposed change, then check whether it parses, applies to the repository, passes an available compile or test command, and stays within the files you authorized. Those checks answer different questions; none proves the design is right.

What a patch score should tell you

A patch is a hypothesis about how to change a codebase, not a completed fix. A useful evaluation records separate results for:

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  • Parsing: Does the output have the structure of a unified diff your tooling can read?
  • Applicability: Does the diff fit the current repository state?
  • Compilation or tests: Does the project’s chosen command accept the change?
  • Scope: Are all changed files on an explicit allowlist?
  • Review: Does the change implement the requested behavior safely and sensibly?

Keep these as separate gates rather than collapsing them into one “score.” A patch can apply cleanly but implement the wrong behavior; it can compile while violating the intended design. Conversely, a missing compile command is unknown evidence, not a passing result.

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Run mechanical checks against the repository

1. Keep the experiment isolated

Evaluate generated changes in a temporary Git worktree or another disposable checkout, rather than applying unreviewed hunks directly to your main working branch. This limits the cost of a malformed or overbroad patch and gives each attempt a known repository state.

2. Check that the diff is parseable

Have the harness parse the unified diff and record a clear failure if its structure is invalid. Parsing only establishes that the input can be interpreted as a patch; it does not establish that the patch applies or that its edits are correct.

3. Ask Git whether it applies

Run git apply --check with the patch in the isolated checkout. Git documents this option as checking whether a patch is applicable to the current working tree and/or index and detecting errors; it does not apply the patch. See Git’s apply documentation.

If the check fails, preserve Git’s rejection output as a diagnostic. Do not report the patch as applicable merely because the model described the change confidently.

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4. Apply and run a project check

After the applicability check, apply the patch in the disposable checkout and run the project’s relevant compile, test, or type-check command if one is available. Record the exact command and its exit status. These checks provide different evidence from applicability: a syntactically applicable patch may still fail to compile or test.

If no relevant command is available, mark compilation or testing as unknown, not successful. A green result from one narrow command also should not be presented as proof that the full test suite or every behavior is correct.

5. Enforce the allowed-file boundary

Compare every path touched by the diff with a declared allowlist. Count or list unexpected paths and make any out-of-scope edit a hard failure when the task requires a strict boundary. For example, if a task authorizes one Python module, an accompanying README edit is still a scope violation even if the code change applies.

Make the result useful for a retry

A compact result should keep the gates and their evidence distinct. For each candidate patch, capture whether parsing and applicability passed, which compile or test command ran and its outcome (or “unknown”), the files and hunks touched, any paths outside the allowlist, and actionable diagnostics. When a patch fails, give the next attempt the actual Git rejection or compiler output rather than a vague “try again.” If it exceeds the permitted file set, reject or remove the out-of-scope changes instead of quietly accepting them.

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Example fixture outputs can illustrate how such a harness behaves, but they are not comparative model-performance data. Results on a real repository depend on its state, commands, and task; a sample score does not predict another project’s outcome.

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What the gates cannot decide

Mechanical checks do not establish that a patch fulfills the request, preserves security properties, has acceptable latency, or is the right design. A reviewer still needs to inspect the diff in context and decide whether the behavior and scope are appropriate. As the original article’s author, Jordan Liu, puts it: “Apply-and-compile is necessary. It is not sufficient.”

The workflow is most defensible when the change is small, the authorized paths are explicit, and an appropriate project check exists. Liu advises against sending generated-code changes, lockfiles, or secrets to a free endpoint, against relying on this approach where a contractual SLA is required, and against treating the gate as a substitute for review. Those are the author’s cautions, not guarantees that a particular service or workflow is suitable for every organization.

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