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arXiv · 2409.18952

RepairBench: Leaderboard of Frontier Models for Program Repair

André Silva·Martin Monperrus
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Abstract

AI-driven program repair uses AI models to repair buggy software by producing patches. Rapid advancements in AI surely impact state-of-the-art performance of program repair. Yet, grasping this progress requires frequent and standardized evaluations. We propose RepairBench, a novel leaderboard for AI-driven program repair. The key characteristics of RepairBench are: 1) it is execution-based: all patches are compiled and executed against a test suite, 2) it assesses frontier models in a frequent and standardized way. RepairBench leverages two high-quality benchmarks, Defects4J and GitBug-Java, to evaluate frontier models against real-world program repair tasks. We publicly release the evaluation framework of RepairBench. We will update the leaderboard as new frontier models are released.

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André Silva, Martin Monperrus. 2024-09-27. RepairBench: Leaderboard of Frontier Models for Program Repair. https://doi.org/10.1109/llm4code66737.2025.00006

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