NDLI: Lightweight data race detection for production runs

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Lightweight data race detection for production runs

Content Provider	ACM Digital Library
Author	Cao, Man Bond, Michael D. Wood, Benjamin P. Zhang, Minjia Biswas, Swarnendu
Abstract	To detect data races that harm production systems, program analysis must target production runs. However, sound and precise data race detection adds too much run-time overhead for use in production systems. Even existing approaches that provide soundness or precision incur significant limitations. This work addresses the need for soundness (no missed races) and precision (no false races) by introducing novel, efficient production-time analyses that address each need separately. (1) Precise data race detection is useful for developers, who want to fix bugs but loathe false positives. We introduce a precise analysis called RaceChaser that provides low, bounded run-time overhead. (2) Sound race detection benefits analyses and tools whose correctness relies on knowledge of all potential data races. We present a sound, efficient approach called Caper that combines static and dynamic analysis to catch all data races in observed runs. RaceChaser and Caper are useful not only on their own; we introduce a framework that combines these analyses, using Caper as a sound filter for precise data race detection by RaceChaser. Our evaluation shows that RaceChaser and Caper are efficient and effective, and compare favorably with existing state-of-the-art approaches. These results suggest that RaceChaser and Caper enable practical data race detection that is precise and sound, respectively, ultimately leading to more reliable software systems. .
Starting Page	11
Ending Page	21
Page Count	11
File Format	PDF
ISBN	9781450352338
DOI	10.1145/3033019.3033020
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-02-05
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Dynamic analysis Data races Sampling Escape analysis
Content Type	Text
Resource Type	Article

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