RacerF: Lightweight Static Data Race Detection for C Code

Fuente: arXiv
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Autores principales: Dacík, Tomáš, Vojnar, Tomáš
Formato: Preprint
Publicado: 2025
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author Dacík, Tomáš
Vojnar, Tomáš
author_facet Dacík, Tomáš
Vojnar, Tomáš
contents We present a novel static analysis for thread-modular data race detection. Our approach exploits static analysis of sequential program behaviour whose results are generalised for multi-threaded programs using a combination of lightweight under- and over-approximating methods. We have implemented this approach in a new tool called RacerF as a plugin of the Frama-C platform. RacerF can leverage several analysis backends, most notably the Frama-C's abstract interpreter EVA. Although our methods are mostly heuristic without providing formal guarantees, our experimental evaluation shows that even for intricate programs, RacerF can provide very precise results competitive with more heavy-weight approaches while being faster than them.
format Preprint
id arxiv_https___arxiv_org_abs_2502_04905
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle RacerF: Lightweight Static Data Race Detection for C Code
Dacík, Tomáš
Vojnar, Tomáš
Software Engineering
Programming Languages
We present a novel static analysis for thread-modular data race detection. Our approach exploits static analysis of sequential program behaviour whose results are generalised for multi-threaded programs using a combination of lightweight under- and over-approximating methods. We have implemented this approach in a new tool called RacerF as a plugin of the Frama-C platform. RacerF can leverage several analysis backends, most notably the Frama-C's abstract interpreter EVA. Although our methods are mostly heuristic without providing formal guarantees, our experimental evaluation shows that even for intricate programs, RacerF can provide very precise results competitive with more heavy-weight approaches while being faster than them.
title RacerF: Lightweight Static Data Race Detection for C Code
topic Software Engineering
Programming Languages
url https://arxiv.org/abs/2502.04905