arXiv CS
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BenchChecker: Assessing the Credibility of Bug-Fixing Benchmarks for LLMs
While Large Language Models (LLMs) show promise for Automatic Program Repair (APR), existing benchmarks suffer from data contamination, in which test samples appear in training sets, artificially inflating performance. To address this, we propose BenchChecker, a framework compatible with both open-s
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