高精度的大规模程序数据竞争检测方法

随着技术的不断发展,软件系统的非确定性(uncertainty)不断增强,数据竞争是并发系统这一类典型的非确定性软件系统中常见的缺陷.尽管数据竞争静态检测近年来取得了巨大进展,但其面临的重要问题仍然存在.先前的静态技术要么以分析精度为代价达到高扩展性,要么由于高精度分析而导致可扩展性问题.提出一种解决上述矛盾的分段分析...

High-precision Data Race Detection Method for Large Scale Programs

With the development of techniques, the uncertainty in software systems are continuously increasing. Data race is a typical bug in current programs, which is a classic type of uncertainty programs. Despite significant progress in recent years, the important problem of practical static race detection remains open. Previous static techniques either suffer from a high false positive rate due to the compromise of precision, or scalability issues caused by a highly precise analysis. In this paper, we present GUARD, a staged approach to resolve this paradox. It first performs a lightweight context-sensitive data access analysis, based on the value flow of a program, to identify the candidate data race subpaths instead of the whole program paths. Second, we employ May-Happen-in-Parallel (MHP) analysis to identify whether two data accesses in a program may execute concurrently. This stage is scalable, due to the design of the thread flow graph (TFG), that encodes thread information to query MHP relationship of the subpaths. Finally, for each subpath whose two data accesses are MHP, we apply heavyweight path-sensitive analysis to verify the feasibility of the data races. The evaluation demonstrates that GUARD can finish checking industrial-sized projects, up to 1.3MLoC, in 1,870 seconds with an average false positive rate of 16.0%. Moreover, GUARD is faster than state-of-the-art techniques with the average speedup 6.08X and significantly fewer false positives. Besides, GUARD has found 12 new race bugs in real-world programs. We report all of them to the developers and 8 of them have been confirmed.