一句话：提出利用大语言模型辅助部分程序依赖分析，缓解不完整代码片段上精度与召回率的权衡。
依赖分析在代码优化与调试中至关重要，但传统方法要求完整的源代码。实际开发中，StackOverflow 论坛或模块化开发常出现不完整或部分代码片段，缺失程序构造，使得依赖分析工具难以正确识别依赖。现有面向部分代码的依赖分析工具往往在精度与召回率之间面临权衡。本文提出利用大语言模型辅助部分程序依赖分析，借助 LLM 的能力处理不完整代码，以期缓解上述权衡。摘要未提供具体方法细节与实验评估结果。
Dependence analysis (DA) plays a critical role in software engineering, from code optimization to debugging. It is traditionally limited to scenarios where entire source code is available. In practice, however, developers often encounter incomplete or partial code snippets, as in StackOverflow (S/O) forums or during modular development, where program constructs are missing. This presents challenges for DA tools, which rely on syntactic and semantic correctness to correctly identify dependencies. Thus, existing DA tools for partial code often face trade-offs in precision and recall.