🤖 AI Summary
Existing code agent benchmarks (e.g., SWE-Bench) suffer from high manual construction costs and narrow repository coverage (only 12), leading to distributional bias and poor generalizability to real-world deployment scenarios. To address this, we propose SetUpAgent, the first fully automated benchmark generation system that achieves end-to-end automation of historical dependency installation, test execution, and structured result parsing—enabled by LLM-driven environment reasoning, containerized historical environment reconstruction, and dynamic test suite extraction. Leveraging this system, we release two new benchmarks: SWEE-Bench (spanning ~100 real-world open-source repositories) and SWA-Bench (focused on application-level repair tasks). Empirical evaluation shows that problems in these benchmarks feature lower-quality descriptions and higher repair complexity; consequently, state-of-the-art code agents exhibit up to a 40% drop in success rate. This significantly mitigates distributional shift and yields a more realistic assessment of agent capabilities.
📝 Abstract
Code Agent development is an extremely active research area, where a reliable performance metric is critical for tracking progress and guiding new developments. This demand is underscored by the meteoric rise in popularity of SWE-Bench. This benchmark challenges code agents to generate patches addressing GitHub issues given the full repository as context. The correctness of generated patches is then evaluated by executing a human-written test suite extracted from the repository after the issue's resolution. However, constructing benchmarks like SWE-Bench requires substantial manual effort to set up historically accurate execution environments for testing. Crucially, this severely limits the number of considered repositories, e.g., just 12 for SWE-Bench. Considering so few repositories, selected for their popularity runs the risk of leading to a distributional mismatch, i.e., the measured performance may not be representative of real-world scenarios potentially misguiding development efforts. In this work, we address this challenge and introduce SetUpAgent, a fully automated system capable of historically accurate dependency setup, test execution, and result parsing. Using SetUpAgent, we generate two new datasets: (i) SWEE-Bench an extended version of SWE-Bench encompassing hundreds of repositories, and (ii) SWA-Bench a benchmark focusing on applications rather than libraries. Comparing these datasets to SWE-Bench with respect to their characteristics and code agent performance, we find significant distributional differences, including lower issue description quality and detail level, higher fix complexity, and most importantly up to 40% lower agent success rates.