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An AI-generated patch is ready to merge only when it satisfies an explicit behavioral contract, passes tests for the requested change and relevant regressions, and produces repeatable evidence from a known code state. A green run means the selected checks passed in the recorded environment; it does not prove universal correctness, security, or safety.
This guide shows how to build that evidence trail, how to detect tests that can be gamed, and how to review a patch when its output looks plausible but its behavior has not been demonstrated.
What a contract harness actually proves
A contract harness is a small, repeatable evaluation around a repository patch. It defines observable behavior, runs checks against a baseline and the patched code, and records enough context for another reviewer to reproduce the result. The harness is not an AI detector and it is not a substitute for code review. Its purpose is to replace “the diff looks reasonable” with evidence tied to the issue being fixed.
The central distinction is between two test sets:
- Acceptance checks exercise behavior that was wrong or missing before the patch and should work afterward.
- Regression checks exercise behavior that already worked and should continue to work.
This mirrors the evaluation pattern described by OpenAI for SWE-bench: tests that fail before a fix and pass after it check the requested change, while tests that pass before and after check that unrelated behavior remains intact. OpenAI states, “Both sets of tests are required to pass for the edit to fully resolve the original GitHub issue.” See OpenAI’s explanation of SWE-bench evaluation.
A pass therefore has a precise meaning: the selected checks passed for one commit, dependency set, runtime, operating environment, and command sequence. It does not establish that every input is handled, that no security issue exists, or that the patch works under an untested deployment configuration.
Start with a behavioral contract, not the proposed implementation
Write the contract before judging the patch. Describe what a user, caller, operator, or downstream service can observe. Do not require a particular private function, file layout, class name, or algorithm unless that structure is itself part of a public compatibility promise.
| Contract element | Question to answer | Observable assertion | Brittle alternative to avoid |
|---|---|---|---|
| Inputs | Which values, files, requests, or events are valid? | Given a UTF-8 document containing an em dash, the parser returns one token for the em dash. | “The patch must call parse_token_v2().” |
| Normal output | What result or side effect should occur? | A valid request returns HTTP 201 and a JSON object containing the created identifier. | Checking an internal temporary variable. |
| Errors | How should invalid or unavailable inputs fail? | A missing configuration value produces a documented error code and does not write a partial file. | Accepting any exception because the process did not crash. |
| Boundaries | What happens at empty, minimum, maximum, duplicate, or truncated values? | An empty list is accepted and returns an empty result, while a list over the documented limit is rejected before storage. | Testing only one ordinary, non-empty example. |
| Compatibility | Which existing behavior must remain unchanged? | Existing callers can omit the new optional field and receive the previous response shape. | Requiring every caller to use a new private parameter. |
Keep each assertion narrow enough to diagnose a failure but broad enough to allow multiple correct implementations. Overly narrow tests can reject a functionally correct patch; vague tests can let an incomplete patch pass. OpenAI’s benchmark discussions identify underspecified tasks, misaligned tests, and environment failures as threats to evaluation validity. See its audit of SWE-bench Verified and its later discussion of coding-evaluation signal.
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Run the same harness against the unmodified base commit and the candidate patch. That establishes which acceptance checks genuinely expose the issue, which regressions already exist, and whether a failure comes from setup rather than the change.
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Freeze the exact base commit
Record the repository URL, branch or pull-request reference, full base commit ID, and patch commit ID when available. Do not evaluate a moving branch. Save the diff with a stable filename so the artifact can be tied to the result.
git rev-parse HEAD git diff --binary <base-commit>..HEAD > candidate.patch -
Prepare a clean working copy
Use a fresh checkout or an isolated worktree. Remove generated files and stale build output. If the project uses a lockfile, install from that lockfile rather than resolving new versions during the run.
git worktree add ../patch-base <base-commit> git worktree add ../patch-candidate <patch-commit> -
Run the baseline checks
Execute the repository’s documented test command and the targeted acceptance tests on the base commit. A properly designed acceptance test should fail for the known bug. If it passes, stop and investigate: the test may not reproduce the issue, the base may already contain a fix, or the expected behavior may be wrong.
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Write the contract as executable checks
Turn each observable requirement into a test with a descriptive name. Include the expected result, error behavior, and relevant boundary cases. Keep fixtures close to the contract so a reviewer can see why each assertion exists.
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Run the candidate patch
Apply or check out the patch in the isolated candidate worktree. Run exactly the same setup and command sequence. Do not silently add a dependency, change an environment variable, or skip a test because it is inconvenient.
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Compare acceptance and regression results
Acceptance checks should move from the documented failure to the documented success. Regression checks should remain green. Any change in a previously passing check needs an explanation, even if the changed behavior appears unrelated.
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Inspect the diff after the run
Review files changed, generated artifacts, configuration edits, dependency changes, migrations, and test modifications. A patch can satisfy a narrow test by altering fixtures, weakening validation, or bypassing the production path.
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Publish an evidence bundle
Store the commit IDs, patch, commands, dependency and runtime versions, environment details, test output, exit codes, and timestamps together. A reviewer should be able to identify exactly what was executed without relying on an interactive session.
Design acceptance tests that demonstrate the fix
One happy-path assertion rarely captures a real issue. Build a small matrix around the behavior named in the contract.
Exercise representative inputs
Include ordinary valid input, the smallest valid input, the largest supported input, and values that previously triggered the bug. For parsers and serializers, vary encoding, whitespace, ordering, duplicate fields, and missing optional fields where those dimensions matter. For APIs, test the documented status code, response shape, headers, and persistence effect rather than only a non-error response.
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Test negative and partial-failure paths
Verify that malformed input is rejected in the intended way, that permission or dependency failures do not produce misleading success, and that retries do not leave duplicate records or half-written files. If the issue concerns an exception, assert its type or stable error code and the externally visible cleanup behavior; avoid asserting incidental wording unless the wording is a compatibility requirement.
Probe boundaries and invariants
Boundary tests should target limits named by the contract: zero items, one item, the maximum allowed count, a value just over the limit, expired timestamps, and repeated operations. Invariants such as “a read does not mutate data” or “normalizing twice gives the same result” can reveal patches that pass one example while violating broader behavior.
Keep tests independent of implementation
Call the public entry point, command, protocol, or user-visible interface whenever possible. A test that imports a private helper can miss integration errors and force an unnecessary design. If a private seam must be tested for speed or isolation, pair it with at least one end-to-end check through the supported interface.
Make the failure meaningful on the base commit
The baseline failure should identify the contract violation, not an unrelated missing package or flaky network service. If the test fails for the wrong reason, fix the fixture or environment before evaluating the patch. Otherwise, a candidate that merely changes setup could appear to “solve” the issue.
Build regression coverage around what must not change
Regression checks are more than rerunning the entire suite. Select behavior adjacent to the changed code and behavior that the issue description explicitly says must remain compatible.
- Run existing tests for the touched module and its callers.
- Exercise both old and new forms of an input when the patch extends a format or API.
- Check persistence, logging, metrics, and cleanup when the change crosses process or storage boundaries.
- Run integration or command-line tests when a unit-level fix could bypass wiring problems.
- Compare serialized output, exit codes, and schema versions for clients that depend on exact interfaces.
Use the full project suite when its runtime and dependencies are available, but label what it does and does not cover. A green full suite can still miss an unrepresented edge case; a targeted failure can reveal a real compatibility break that a broad suite does not exercise.
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Make the environment reproducible and attributable
Environment setup is part of the evaluation, not background detail. OpenAI’s SWE-bench documentation discusses Docker-based execution as a way to improve reproducibility, and the SWE-bench project documents its evaluation workflow at the project repository. Containerization is useful, but no single harness architecture fits every repository.
Record these fields for every run:
- Base and candidate commit IDs, patch checksum, and repository state.
- Operating-system image, container image digest or machine type, CPU architecture, and relevant kernel details.
- Language runtime, compiler, package manager, dependency lockfile checksum, and database or service versions.
- Environment variables that affect behavior, with secrets removed or replaced by documented placeholders.
- Locale, timezone, clock configuration, network policy, filesystem type, and external-service stubs.
- Exact setup and test commands, working directory, timeout values, retry policy, exit codes, and complete logs.
Pin nondeterministic inputs where practical. Use fixed fixture data, a controlled clock, deterministic random seeds, and local service doubles. If a test depends on a live service, record the service version and availability and distinguish an infrastructure failure from a patch failure. Never rerun only the failing test until it passes and then report the result as stable; preserve every attempt.
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A patch can pass a weak contract without delivering the requested outcome. Before accepting a green result, read the test as adversarially as you read the patch.
Look for vacuous assertions
An assertion that checks only “no exception,” a non-empty response, or a truthy value may pass when the result is wrong. Assert the fields, side effects, and error semantics that define success.
Check that the test reaches production code
Excessive mocking can allow a patch to satisfy a mocked call while the real integration remains broken. Verify that the fixture invokes the same public path used in production and that mocks are limited to unavailable or deliberately isolated dependencies.
Detect expected-value contamination
If the test computes its expected value with the same new helper or reads the output it is supposed to verify, both the implementation and assertion can share the same defect. Keep critical expected values independent, such as fixed fixtures, documented examples, or a separately implemented oracle.
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Inspect changed tests and fixtures
Removing a failing case, broadening an expected exception, changing a fixture to match the patch, or disabling a timeout is a behavior change in the harness. Review test diffs with the same scrutiny as production diffs.
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Ask whether the contract is too narrow or too broad
A narrow test checks only one implementation route and can reject valid solutions. A broad test that accepts many incorrect outputs can inflate pass rates. OpenAI’s 2026 audit classified 35.5% of a selected 138-task sample as having narrow tests and 18.8% as having wide tests. Those percentages describe that audited sample only, not all software tests or AI patches; details are in the audit report.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use a harness level that matches the risk
There is a trade-off between isolation, coverage, speed, and maintenance. Choose deliberately rather than treating a single test command as a universal answer.
| Harness level | Best use | Evidence strength | Cost and limitations |
|---|---|---|---|
| Targeted local checks | Fast iteration on a small, well-understood contract. | Good feedback on the named behavior; weak evidence about integration and environment drift. | Low runtime and setup cost; easy to run with accidental local state. |
| Repository suite in a clean worktree | Changes that touch established modules and callers. | Combines targeted acceptance tests with existing regressions. | Moderate runtime; coverage still depends on the repository’s test quality. |
| Containerized or otherwise isolated run | Patches sensitive to dependencies, services, operating systems, or build tools. | Stronger attribution because setup is captured and repeatable. | Higher image-maintenance cost; containers do not automatically fix flawed contracts. |
| Benchmark-style evaluation | Comparing many agents or patches under a common protocol. | Useful only when tasks, tests, and contamination controls are credible. | Expensive curation and execution; aggregate scores can hide task and test defects. |
No framework, CI service, language, or agent is required by the contract-harness method. The important properties are observable criteria, both acceptance and regression coverage, repeatable execution, implementation-independent assertions, and a runtime cost appropriate to the risk.
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| Observed result | Likely explanation | Next action |
|---|---|---|
| Acceptance test passes on the base commit | The test does not reproduce the issue, the base is already fixed, or the contract is incorrect. | Reconfirm the issue and expected behavior before judging the candidate. |
| Acceptance fails on both base and candidate | The patch did not fix the contract, or setup prevents the test from reaching the code. | Inspect the assertion and environment; classify the failure before changing the patch. |
| Acceptance passes but a regression fails | The requested behavior was added with an incompatible side effect. | Review the changed interface and decide whether compatibility must be restored or the contract intentionally changed. |
| Only an external-service test fails | Network, credentials, service version, or rate limits may be responsible. | Reproduce with a controlled stub or documented service version; do not call it a code failure without attribution. |
| Results vary between identical runs | Race conditions, time, randomness, ordering, shared state, or resource pressure. | Capture seeds and timing, isolate state, and repeat enough to characterize flakiness. |
| Tests pass after broad fixture or test edits | The harness may have been weakened. | Review test and fixture diffs, restore independent expected values, and rerun the original contract. |
Review the patch beyond its test output
Behavioral evidence does not eliminate human review. Read the diff for changes that the contract does not authorize:
- Unrelated files, generated artifacts, dependency upgrades, or configuration changes.
- Broader permissions, disabled validation, swallowed exceptions, or logging of sensitive data.
- New network calls, shell execution, deserialization, dynamic evaluation, or filesystem writes.
- Changes to migrations, retry behavior, transactions, concurrency, or resource limits.
- Compatibility breaks hidden by updated fixtures or a test that covers only one client.
Ask whether the patch fixes the underlying path or merely special-cases the provided example. Compare the implementation with the contract’s boundaries and error cases. If the issue involves security, privacy, authorization, or data integrity, add explicit abuse and failure tests; ordinary functional tests are not a security review.
What benchmark numbers can—and cannot—tell you
Public coding-agent scores need methodological context. The SWE-bench dataset was described by Aleithan and colleagues in 2024 as covering 2,294 real-world GitHub issues across 12 Python repositories. That figure describes the dataset in that paper, not a current benchmark size. Their SWE-Bench+ paper also reported solution leakage and weak tests after manual screening; its percentages depend on the authors’ instance selection and evaluated setup.
OpenAI described creating SWE-bench Verified by having three experts independently review 1,699 problems and curating 500. In a later audit of 138 selected problems that an o3 system did not consistently solve over 64 independent runs, OpenAI reported material issues in test design and/or problem descriptions in 59.4% of cases. The same audit categorized 35.5% as having narrow tests and 18.8% as having wide tests. These are bounded audit results, not universal defect rates.
A separate 2024 study examined 4,892 patches from 10 agents across 500 GitHub issues and found no single agent dominated; it also highlighted test-coverage limitations. Another analysis reported solution leakage in 32.67% of successful patches and judged 31.08% of passed patches suspicious because of weak tests. Those figures are study-specific findings, not a general probability that an AI patch is wrong. See the patch-patterns study for its scope and screening method.
OpenAI said it stopped reporting SWE-bench Verified scores after identifying test-design flaws and contamination concerns, and at that time recommended SWE-bench Pro pending uncontaminated evaluations. A later 2026 discussion retracted that recommendation after concerns about SWE-Bench Pro and called for benchmarks developed by experienced software developers with human oversight. Because those recommendations changed, verify the current status before using any leaderboard claim. The broader principle is captured in OpenAI’s statement: “Ultimately, an eval should provide meaningful signal through benchmarks that are hard to game, easy to trust, and genuinely reflective of model capability or alignment.” See the full discussion.
A release checklist for an AI-generated patch
- The issue has a written behavioral contract with normal, error, boundary, and compatibility cases.
- The acceptance test fails on the frozen base commit for the intended reason.
- The candidate passes acceptance checks without weakening their assertions or fixtures.
- Relevant existing behavior passes regression checks on the candidate.
- Tests exercise supported public interfaces and do not require incidental private structure.
- Expected values are independent of the implementation under test.
- The diff contains no unexplained files, permissions, dependency, configuration, or data changes.
- Runtime, dependencies, services, locale, timezone, randomness, and network policy are recorded.
- Commands, logs, exit codes, timestamps, commit IDs, and patch artifacts are retained.
- Failures are classified as contract, regression, environment, or nondeterminism issues rather than collapsed into “red” or “green.”
- Human review covers security, privacy, performance, concurrency, and operational effects that the automated contract does not exercise.
When these conditions are met, the harness gives reviewers a defensible answer to “does this patch satisfy the requested behavior under known conditions?” That is a much stronger question than whether the generated code looks plausible, while remaining honest about what the tests did not run.
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