Affiliation:
1. Ohio State University, USA
Abstract
Data races are a real problem for parallel software, yet hard to detect. Sound predictive analysis observes a program execution and detects data races that exist in some other, unobserved execution. However, existing predictive analyses miss races because they do not scale to full program executions or do not precisely incorporate data and control dependence.
This paper introduces two novel, sound predictive approaches that incorporate data and control dependence and handle full program executions. An evaluation using real, large Java programs shows that these approaches detect more data races than the closest related approaches, thus advancing the state of the art in sound predictive race detection.
Funder
National Science Foundation
Publisher
Association for Computing Machinery (ACM)
Subject
Safety, Risk, Reliability and Quality,Software
Cited by
9 articles.
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2. Sound Predictive Fuzzing for Multi-threaded Programs;2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC);2023-06
3. Tolerate Control-Flow Changes for Sound Data Race Prediction;2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE);2023-05
4. A tree clock data structure for causal orderings in concurrent executions;Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems;2022-02-22
5. Sound and efficient concurrency bug prediction;Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering;2021-08-18