一句话：提出 NeuDep，用神经网络预测二进制指令间的内存依赖，以缓解静态别名分析的保守性与可扩展性问题。
论文研究二进制内存依赖分析：判断多条指令是否可能访问同一内存位置。该问题理论上因精确别名分析不可判定而困难，在二进制级别又因缺少符号与类型信息、以及编译器优化而加剧；现有方法要么因保守分析产生大量伪依赖，要么难以扩展到复杂二进制。作者提出 NeuDep，一种基于神经网络的方法，通过从二进制代码中学习模式来预测指令间的内存依赖，避免依赖传统精确静态别名计算。摘要未报告具体实验数据与结果，但其目标是在真实复杂二进制上同时改善依赖判定的准确性与可扩展性。
Determining whether multiple instructions can access the same memory location is a critical task in binary analysis. It is challenging as statically computing precise alias information is undecidable in theory. The problem aggravates at the binary level due to the presence of compiler optimizations and the absence of symbols and types. Existing approaches either produce significant spurious dependencies due to conservative analysis or scale poorly to complex binaries.
@inproceedings{paperbot7077,
title = {NeuDep: neural binary memory dependence analysis},
author = {Kexin Pei and Dongdong She and Michael Wang and Scott Geng and Zhou Xuan and Yaniv David and Junfeng Yang and Suman Jana and Baishakhi Ray},
booktitle = {FSE},
year = {2022},
doi = {10.1145/3540250.3549147}
}