Ruikai Shi, Zhengran Zeng, Rui Xie, Shikun Zhang, Wei Ye · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.25308 · cited by 0 works (OpenAlex) · open-access PDF
ソフトウェアエージェントがより効率的にコードを生成し変更するにつれて、変更をレビューし欠陥を発見する能力を評価することが重要になります。そのような評価には、再生可能な行動によってサポートされる調査および信頼できるタスク欠陥参照のための実行可能なプロジェクト環境が必要です。既存のコードレビューベンチマークは、この組み合わせのための限られたサポートを提供します。我々は、実行可能な再生および証拠ベースのレビューインスタンスを歴史的な導入・修正コミットパイアおよび開発者テストから構築するシステムを提示します。システムは、修正側の変更から候補テストを抽出し、それらを移植し、バージョン特定の環境を構成します。生産コードと
Machine translation (Meta M2M100); the English original follows.
As software agents increasingly generate and modify code, evaluating their ability to review changes and discover defects becomes important. Such evaluation needs executable project environments for investigation and reliable defect references supported by reproducible behavior. Existing code review benchmarks provide limited support for this combination. We present a system that constructs executable regression instances from historical introducing--fixing commit pairs and developer tests. The system extracts candidate tests from fixing-side changes, then uses constrained agents to transplant them and configure version-specific environments while preserving production code and test expectations. Independent replay and evidence-based review verify whether tests that fail on the introducing version and pass on the fixing version support the proposed regression relationship. Accepted instances retain both versions' replay recipes, validated tests, and execution evidence. Repair-supported analysis and human review add root-cause locations, tested repairs, and causal explanations. These instances support bug detection and localization in code review, and enable bug repair and regression-test generation on the same defects. The system constructs 172 validated instances from 522 candidates across 11 Python projects. We evaluate three software agents with ten models and additional reasoning-effort settings on 83 manually selected, enriched instances across the three tasks. Precise root-line localization remains challenging, while baseline combinations achieve repair and regression-test generation success rates of up to 68.7% and 77.1%, respectively. Leading configurations differ by task, and higher task performance does not necessarily yield greater API-cost efficiency. Our approach provides reusable executable evidence for studying agents' defect discovery, repair, and testing capabilities.
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Topics: Executable, Regression testing, Computer science, Python (programming language), Commit, Software engineering
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