一句话：提出高效强更新方法，在保持路径敏感精度的同时降低指针间接数据依赖分析的开销。
路径敏感数据依赖分析是静态漏洞检测中广泛使用的强力技术。其核心挑战之一在于解析指针操作引发的间接数据依赖：从某内存位置加载的值可能依赖于此前存入的不同值。以路径敏感方式解析这类间接依赖能显著提升分析精度，但也会带来高昂开销，限制分析的可扩展性。本文（据标题）提出面向路径敏感数据依赖分析的高效强更新（strong update）方法，旨在缓解精度与开销之间的矛盾。由于所给摘要仅陈述问题背景，未提供具体方法与实验评估细节，其关键技术方案与实际效果有待查阅正文。
Path-sensitive data dependence analysis is a powerful technique widely used in static vulnerability detection. One of the central challenges is how to resolve indirect data dependencies induced by pointer operations: the value loaded from a memory location may depend on different values stored before. Resolving indirect data dependencies in a path-sensitive manner significantly improves the analysis precision, but also induces high overhead that limits its scalability.