旧代码片段会误导模型生成过时代码，影响代码补全质量。
When Retrieval Hurts Code Completion: A Diagnostic Study of Stale Repository Context
- 通过控制实验对比新旧代码上下文的影响。
- 旧上下文使模型生成过时代码比例上升88.2%和76.5%。
- 适合关注代码生成鲁棒性的开发者与研究者。
检索增强的代码生成依赖跨文件项目上下文，但检索到的代码片段可能来自过时的项目状态。本文针对五个Python仓库中17个生产级辅助函数签名变更的样本，开展受控诊断研究。在隐藏提交新鲜度和预期当前签名的前提下，比较仅当前、仅旧版、无检索及混合新旧检索四种条件下的表现。结果表明：在中性提示下，仅使用旧上下文时，Qwen2.5-Coder-7B-Instruct模型在15/17样本中引入旧版辅助函数引用，比仅用当前上下文提升88.2个百分点；gpt-4.1-mini在13/17样本中出现类似问题，提升76.5个百分点。无检索条件下虽无旧引用，但仅1/17样本生成正确代码。两模型在触发旧引用的样本上具有75.0%的Jaccard重合率。混合条件下加入有效当前证据可显著缓解旧上下文带来的错误。结论：检索上下文的时间有效性是代码RAG鲁棒性的重要诊断变量——过时上下文会主动诱导模型偏向旧项目状态，而非仅损失有效信息。
原文摘要 · Abstract (English)
Context: Retrieval-augmented code generation relies on cross-file repository context, but retrieved snippets may come from obsolete project states. Objectives: We study whether temporally stale repository snippets act as harmless noise or actively induce current-state-incompatible code. Methods: We conduct a controlled diagnostic study on a curated 17-sample set of production-helper signature changes from five Python repositories. For each sample, we compare current-only, stale-only, no-retrieval, and mixed current/stale retrieval conditions under prompts that hide commit freshness and expected current signatures. Results: Under neutralized prompts, stale-only retrieval induces stale helper references on 15/17 Qwen2.5-Coder-7B-Instruct samples and 13/17 gpt-4.1-mini samples, corresponding to 88.2 and 76.5 percentage-point increases over current-only retrieval. No retrieval produces zero stale references but only 1/17 passing completions. The two models share 75.0% Jaccard overlap among stale-triggering samples, and mixed conditions show that adding valid current evidence largely rescues stale-only failures. Conclusion: Temporal validity of retrieved repository context is a distinct diagnostic variable for Code RAG robustness: stale context can actively bias models toward obsolete repository state rather than merely removing useful evidence.
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