← 研踪 PaperTrailarXiv:2510.12082 · v2
Enhancing Neural Code Representation with Additional Context
首次提交 2025-10-14 10:45
版本更新 2026-11-06 14:44
雷达观测 2026-11-09 11:19
摘要
Automated program comprehension underpins many software engineering tasks, from code summarisation to clone detection. While recent deep learning models have achieved strong results, they typically rely on source code alone, overlooking contextual signals such as version history or structural relationships. We conduct an empirical study of how enriching code representations with such signals affects model performance on four tasks: code clone detection, code classification, code summarisation, and vulnerability detection, using three datasets (SeSaMe, CodeSearchNet-Java, Vul4J). We evaluate five foundational code models (CodeBERT, GraphCodeBERT, CodeT5, PLBART, ASTNN) and seven code-specialised LLMs (CodeLlama, Qwen2.5-Coder, DeepSeek-Coder), comparing prompt-based context injection against fine-tuned use of context. Context improves classification for all five foundational models and, after fine-tuning, for most LLMs. For summarisation, the effect depends on the model family: version history improves all four foundational models on every metric, whereas prompt-level context helps two of the seven LLMs and hurts the other five. Combining context types reaches up to +21.48% macro-F1 in code classification and +15.04% F1 in clone detection. A human evaluation of CodeT5 summaries on 100 Java snippets ranks context-augmented summaries significantly higher for Accuracy and Content Adequacy. An exploratory vulnerability detection evaluation provides initial evidence of transfer to security-oriented tasks.
作者备注：Accepted for publication in ACM Transactions on Software Engineering and Methodology (TOSEM). 65 pages
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v2 · 2026-11-06 14:44 · 最新观测版本
Enhancing Neural Code Representation with Additional Context
Automated program comprehension underpins many software engineering tasks, from code summarisation to clone detection. While recent deep learning models have achieved strong results, they typically rely on source code alone, overlooking contextual signals such as version history or structural relationships. We conduct an empirical study of how enriching code representations with such signals affects model performance on four tasks: code clone detection, code classification, code summarisation, and vulnerability detection, using three datasets (SeSaMe, CodeSearchNet-Java, Vul4J). We evaluate five foundational code models (CodeBERT, GraphCodeBERT, CodeT5, PLBART, ASTNN) and seven code-specialised LLMs (CodeLlama, Qwen2.5-Coder, DeepSeek-Coder), comparing prompt-based context injection against fine-tuned use of context. Context improves classification for all five foundational models and, after fine-tuning, for most LLMs. For summarisation, the effect depends on the model family: version history improves all four foundational models on every metric, whereas prompt-level context helps two of the seven LLMs and hurts the other five. Combining context types reaches up to +21.48% macro-F1 in code classification and +15.04% F1 in clone detection. A human evaluation of CodeT5 summaries on 100 Java snippets ranks context-augmented summaries significantly higher for Accuracy and Content Adequacy. An exploratory vulnerability detection evaluation provides initial evidence of transfer to security-oriented tasks.
查看此版本解读 →查看此版本原文 ↗