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原文連結
論文資訊
- 類型：已發表論文
- 日期：2025-02-19
摘要
機器學習 (ML) pervades the field of Automated Program Repair (APR). Algorithms deploy 神經 machine translation and large 語言 models (LLMs) to generate software patches, among other tasks. But, there are important differences between these applications of ML and earlier work, which complicates the task of ensuring that results are valid and likely to generalize. A challenge is that the most popular APR evaluation benchmarks were not designed with ML techniques in mind. This is especially true for LLMs, whose large and often poorly-disclosed training datasets may include problems on which they are evaluated. This article reviews work in APR published in the field's top five venues since 2018, emphasizing emerging trends in the field, including the dramatic rise of ML models, including LLMs. ML-base
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