TestEvo-Bench: An Executable and Live Benchmark for Test and Code Co-Evolution
Score
61Why selected
Proposes an innovative and practical live benchmark with execution metrics and timestamp-based leakage prevention, with solid experiments, though limited to Java projects and moderate scale.
AI commentary
「This benchmark fills the gap of existing test benchmarks that cannot dynamically evaluate test-code co-evolution capabilities, helping advance smarter test automation agents.」
Quick overview
TestEvo-Bench is a benchmark for evaluating test automation agents in test and code co-evolution scenarios. It features two tracks: test generation and test update, with each task anchored to real commit history and packaged with environment configuration to support execution-grounded metrics such as pass rate, coverage, and mutation score. It is a live benchmark that records timestamps and periodically mines new tasks from open-source Java projects, enabling evaluation restricted to tasks postdating a model's training cutoff to reduce data leakage risk. The current snapshot includes 746 test generation and 509 test update tasks. Experiments show state-of-the-art agents achieve up to 77.5% success rate on test generation and 74.6% on test update, but performance drops significantly on recent tasks and under limited per-task cost.
- Introduces TestEvo-Bench, an executable and live benchmark for test and code co-evolution.
- Comprises two tracks: test generation and test update, anchored to real commit history.
- Supports execution-grounded metrics (pass rate, coverage, mutation score) and reduces data leakage via timestamps.
- Experiments show state-of-the-art agents achieve ~75% success rate, but drop on recent tasks and under cost constraints.
Full text
Computer Science > Software Engineering
arXiv:2607.02469v1 (cs) [Submitted on 2 Jul 2026]Title:TestEvo-Bench: An Executable and Live Benchmark for Test and Code Co-Evolution
Authors:Jiale Amber Wang, Kaiyuan Wang, Pengyu Nie View a PDF of the paper titled TestEvo-Bench: An Executable and Live Benchmark for Test and Code Co-Evolution, by Jiale Amber Wang and 2 other authors View PDF HTML (experimental)Abstract:Software tests and code evolve together: a code change should be followed by new or updated tests that record the new software behavior. Yet existing test generation and update benchmarks often isolate the test from the code change, and rely on static metadata that does not verify whether a test is executable or semantically tied to the code change. This makes it difficult to evaluate whether a test automation agent understands how a code change should propagate into the test suite.
We introduce TestEvo-Bench, a benchmark of test and code co-evolution tasks mined from software repositories, with two tracks: in test generation, the agent shall write new tests to capture the new software behavior; in test update, the agent shall adapt failing existing tests to the changed software behavior. Each task is anchored to a real commit history and packaged with environment configuration to support execution-grounded metrics such as pass rate, coverage, and mutation score. TestEvo-Bench is also a live benchmark: each task records the timestamp of the test and code changes, and new tasks are periodically mined by our automated pipeline, so evaluation can be restricted to tasks postdating a model's training cutoff to reduce data leakage risk. The current snapshot contains 746 test generation and 509 test update tasks, curated from 59,950 candidate co-evolution records across 152 open-source Java projects. We experiment with four state-of-the-art agents that combine strong harnesses (Claude Code, Gemini CLI, and SWE-Agent) with strong foundation models (Claude Opus 4.7 and Gemini 3.1 Pro). Results show that they achieve up to 77.5% success rate on test generation and 74.6% on test update. However, success rate is materially lower on the most recent benchmark tasks and drops significantly under limited per-task cost.
| Comments: | TestEvo-Bench leaderboard and data explorer are hosted at this https URL |
| Subjects: | Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2607.02469 [cs.SE] |
| (or arXiv:2607.02469v1 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2607.02469 Focus to learn more arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Pengyu Nie [view email][v1] Thu, 2 Jul 2026 17:35:20 UTC (1,039 KB)
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