解决代码检索中的文本偏见问题，提升生成质量。
SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization
- 通过语义增强重排序与定位，缓解对表面文本的依赖。
- 在三个数据集上召回率最高提升12.8%，代码生成准确率提升4.88%。
- 适合关注代码生成鲁棒性与检索公平性的研究者。
检索增强型代码生成（RACG）是通过检索相关信息来提升代码生成性能的关键技术。本文系统性地掩码特定特征以保留代码功能，深入分析代码检索机制。发现：（1）当前检索器虽基于代码训练，却严重依赖表面文本特征（如文档字符串、标识符名称）；（2）对文档充分的代码存在显著偏好，即使文档内容无关。基于此，我们提出SACL框架，通过引入语义信息增强代码或结构知识，减少偏见。大量实验表明，SACL显著提升代码检索性能（如HumanEval / MBPP / SWE-Bench-Lite上Recall@1分别提升12.8% / 9.4% / 7.0%），并进一步改善代码生成效果（HumanEval上Pass@1提升4.88%）。
原文摘要 · Abstract (English)
Retrieval-Augmented Code Generation (RACG) is a critical technique for enhancing code generation by retrieving relevant information. In this work, we conduct an in-depth analysis of code retrieval by systematically masking specific features while preserving code functionality. Our discoveries include: (1) although trained on code, current retrievers heavily rely on surface-level textual features (e.g., docstrings, identifier names), and (2) they exhibit a strong bias towards well-documented code, even if the documentation is irrelevant. Based on our discoveries, we propose SACL, a framework that enriches textual information and reduces bias by augmenting code or structural knowledge with semantic information. Extensive experiments show that SACL substantially improves code retrieval (e.g., by 12.8% / 9.4% / 7.0% Recall@1 on HumanEval / MBPP / SWE-Bench-Lite), which also leads to better code generation performance (e.g., by 4.88% Pass@1 on HumanEval).
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