ADAPT: Agentic Diff-Aware Patch Testing for Pull Requests
Story 5, from arXiv
ADAPT is a new tool that uses an autonomous coding agent to generate focused regression tests for pull requests, achieving higher success rates than existing methods.
Key points
- ADAPT uses an autonomous coding agent to generate tests focused on the changes in a pull request.
- The agent analyzes diffs to identify modified functions and generates candidate unit tests.
- A fail-to-pass (F2P) tool verifies tests by running them on both versions of the code.
- ADAPT achieves 62.0% F2P success with GPT-4o and 89.9% with GPT-5-mini on real-world PRs.
- On 137 PRs, ADAPT covers 83.9% of changes, outperforming ChaCo's 79.6%.
- Manual evaluation shows tests are important, strong, and readable, often covering more than developer-written tests.
Why it matters
Software engineers need reliable regression tests for pull requests to prevent future bugs. ADAPT offers a more effective way to generate these tests, improving code quality and reducing the risk of regressions.
Counterpoint
ADAPT's effectiveness depends on the quality of the underlying LLM, and its performance may vary with different models and codebases.
Sources
This summary was written by a language model and may contain errors. Read the original for the full story.