RacerX

Author:

Engler Dawson1,Ashcraft Ken1

Affiliation:

1. Stanford University, Stanford, CA

Abstract

This paper describes RacerX, a static tool that uses flow-sensitive, interprocedural analysis to detect both race conditions and deadlocks. It is explicitly designed to find errors in large, complex multithreaded systems. It aggressively infers checking information such as which locks protect which operations, which code contexts are multithreaded, and which shared accesses are dangerous. It tracks a set of code features which it uses to sort errors both from most to least severe. It uses novel techniques to counter the impact of analysis mistakes. The tool is fast, requiring between 2-14 minutes to analyze a 1.8 million line system. We have applied it to Linux, FreeBSD, and a large commercial code base, finding serious errors in all of them. RacerX is a static tool that uses flow-sensitive, interprocedural analysis to detect both race conditions and deadlocks. It uses novel strategies to infer checking information such as which locks protect which operations, which code contexts are multithreaded, and which shared accesses are dangerous. We applied it to FreeBSD, Linux and a large commercial code base and found serious errors in all of them.

Publisher

Association for Computing Machinery (ACM)

Cited by 241 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Accurate Static Data Race Detection for C;Lecture Notes in Computer Science;2024-09-11

2. SSRD: Shapes and Summaries for Race Detection in Concurrent Data Structures;Proceedings of the 2024 ACM SIGPLAN International Symposium on Memory Management;2024-06-20

3. Efficient data race detection for interrupt-driven programs via path feasibility analysis;The Journal of Supercomputing;2024-06-14

4. Understanding and Detecting Real-World Safety Issues in Rust;IEEE Transactions on Software Engineering;2024-06

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