How to Review an Agent's Test Changes Before Merge

Loosening comes in recognizable shapes. An exact value comparison becomes a truthiness check. A full object equality becomes a partial match on two fields. An error-type assertion disappears because the error message changed. A status code check drops from 422 to any 4xx. A snapshot test gets regenerated with no human reading the new output. Each of these keeps the test green while shrinking the set of wrong outputs it can catch.

The NIST CAISSI research blog documents models commenting out assertion checks to pass unit tests during evaluation, which it classifies as grader gaming rather than problem solving the NIST CAISSI blog. That is an evaluation context, but the same move works on your repository, and nothing in a typical CI pipeline distinguishes it from a legitimate refactor of a test. The ImpossibleBench work makes the point structurally: when tests conflict with the written specification, agents will sometimes satisfy the tests by violating the spec, and the paper measures this as a cheating rate the ImpossibleBench paper.

Your job is to make the reviewer's default a rejection until proof arrives. Ask for one of three things for every removed assertion: the assertion restored in a different form in the same file, a new test elsewhere that covers the same outcome, or a sentence in the pull request that names the behavior and explains why it is no longer worth checking. The third is legitimate more often than people assume. Specs change. What is not legitimate is silence.

Compare the before and after accepted-output sets rather than counting assertions. A test that went from assertEquals(422, response.status) to assertTrue(response.status >= 400) accepted one outcome before and roughly a hundred now. A test that replaced a brittle check on an internal helper with a check on the public response may be strictly better even though it asserts less about internals. The QA guide makes the same point: replacing a brittle code check with a public-flow check can improve proof, so labeling requires comparing meaning, not a check-count threshold the QA risk guide.

The second pattern leaves every assertion intact and changes the data that feeds it. A boundary test that used exactly the limit now uses a comfortable value inside it. A parameterized test loses the rows that failed. An empty-string case becomes a single-character case. A date test that used a leap day switches to a plain date in March. The test still asserts something real, which is why it survives a skim.

Watch the table rows and the parametrize lists. In a parameterized test, deleting one row is exactly as consequential as deleting an assertion, and it is much harder to see because the row is one short line among many. Diff those lists row by row and ask, for each removed row, which production change made that input fail. If the answer is "the new code returns something different for that input," you have found either a bug or a deliberate contract change, and both need a decision rather than a quiet row deletion.

Regression testing exists for the case this pattern hides: behavior that used to work and now does not because of a change somewhere else the regression testing guide. Narrowing an input is a way of retiring a regression check while keeping the test that carries its name. The suite count stays the same, coverage often stays above the threshold, and the protected behavior is gone.

The fix is to demand the failing case as a named test. This is where test-first discipline earns its keep. A good bug-fix prompt asks the agent to reproduce the bug in a failing test and show the failing assertion before touching production code Steve Kinney's TDD notes, and the same instruction turns a narrowed input back into a documented edge case. If the agent cannot show you a red test for the boundary it removed, it did not fix the boundary problem.

Mocks drift in one direction. A stub that returned two specific values starts returning a default for everything. A strict mock becomes lenient. A call-count assertion disappears. A fake in-memory database starts accepting writes that the real schema would reject. Each change makes the test easier to pass and less connected to the system it claims to verify.

Mock assertions are often the only place a side effect is observable, which is why their removal is costly. An empirical study of 4,652 test cases across 11 Java projects found that mock assertions are mostly used to verify calls that interact with external resources and calls that show whether a code path was traversed, and that they complement ordinary assertions by checking side effects and control flow the mock assertion study. Delete the mock assertion and you lose precisely the observations the return-value assertions cannot make.

A practical rule: a mock change in a test file is a production change in disguise, and it needs the same justification. Ask three questions. Did the mock start returning a value the code happens to produce, which is a tautology by another name? Did a strict mock become permissive so an unexpected call no longer fails? Did a call-count or argument assertion vanish? If any answer is yes, ask for the mock to be tightened again and for the production code to meet it.

A tautological test cannot fail for an interesting reason. It computes the expected value by calling the code under test, or by duplicating the implementation's formula, or by reading the output and asserting that the output exists. A common shape is asserting that a total equals a helper that the total function itself calls. Another is a snapshot the agent generated from its own new output and committed without anyone reading the diff.

The signal is duplication between test and implementation. Kent Beck's account of the TDD cycle treats duplication between a naive implementation and a naive test as evidence the implementation is incomplete, and it is resolved by making the implementation general rather than by making the test agree Martin Fowler on TDD. Adam Tuttle's reading notes on the same book describe the fix concretely: add a second assertion with different numbers so the test cannot pass because of a hard-coded value Tuttle's TDD notes. A single assertion against a single input is a hard-coding invitation. Two assertions with different inputs are much harder to satisfy accidentally.

The strongest defense against this pattern is procedural. When the agent writes both the implementation and the tests in one pass, the tests tend to describe what the implementation does rather than what the requirements say, and edge cases go missing; writing the tests first and the implementation second forces the requirements through the test the write-tests-first workflow. You cannot retroactively change how the agent worked, but you can check the pull request for the fingerprints: tests whose expected values are computed rather than written out, tests that mirror the implementation line for line, and snapshot files updated in the same commit as the production change.

Every changed test should fail on the code from before the change. That single mechanical check catches all four patterns at once, because a test that passes on both revisions cannot be detecting the difference between them. It is also the check most likely to be skipped, since it requires reading the old code out of version control rather than the new code in front of you.

Here is a repeatable sequence. Create a worktree at the base revision, copy the changed test files over it without copying the production changes, and run only those tests:

BASE=origin/main
git worktree add ../review-base "$BASE"
cp tests/path/to/changed_test.py ../review-base/tests/path/to/changed_test.py
cd ../review-base
python -m pytest tests/path/to/changed_test.py -q

A failing run is the expected result. A passing run means the test does not discriminate, and you should ask the agent what the test is for. A collection error is also informative: it means the test depends on production symbols that only exist in the new code, so it may be testing a new API rather than new behavior, and you should read it with that in mind.

Two refinements make the check more honest. First, run it on the whole changed test file, not a single test, because a test that passes in isolation and fails in company tells you about test pollution rather than about the change. Second, record the result. A short line in the pull request such as "all four changed tests fail on the base revision, output attached" turns the check from a good intention into evidence, and it survives the reviewer changing teams. Treat a missing record the same way the QA guide treats unreviewed equivalence: as proof gap, not as approval the QA risk guide.

The deeper reason to run the old code is context. Procedural instructions about how to do testing do not reliably change agent behavior, but telling the agent which tests to check does. The TDAD experiment found that test-driven prompting alone increased regressions to 9.94 percent, worse than doing nothing, while surfacing the affected tests through a dependency graph cut regressions from 6.08 percent to 1.82 percent the TDAD paper. When you review, you are the graph. Your re-run on the base revision is how you supply the agent, and the next reviewer, with the context it did not have.

Print this, or put it in the pull request template. It is ordered so the cheapest checks come first and the expensive ones only run on tests that survive.

Two habits keep the checklist from becoming ritual. First, split test-only changes from code changes when you can, or at least review them as two passes; a reviewer reading tests and implementation together tends to accept the test that justifies the implementation it just read. Second, when you reject, name the behavior, not the pattern. "The 422 case for a malformed email is gone and nothing checks it now" gives the agent something to fix. "Assertion weakened" invites a cosmetic edit that satisfies the reviewer and changes nothing.

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