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Data Race Detection by Digest-Driven Abstract Interpretation (Extended Version)

2025/11/14 by Michael Schwarz, Schwarz, Michael, Julian Erhard +1 · 1 citation
Computer Science · #Distributed systems and fault tolerance #FOS: Computer and information sciences #Logic, programming, and type systems #Parallel Computing and Optimization Techniques #Programming Languages (cs.PL) #Software Engineering (cs.SE)

paper · pdf · doi:10.48550/arxiv.2511.11055

openalex publication_date 2025/11/14 · openalex created_date 2025/11/18 · openalex updated_date 2026/07/28

Abstract

Sound static analysis can prove the absence of data races by establishing that no two conflicting memory accesses can occur at the same time. We repurpose the concept of digests -- summaries of computational histories originally introduced to bring tunable concurrency-sensitivity to thread-modular value analysis by abstract interpretation, extending this idea to race detection: We use digests to capture the conditions under which conflicting accesses may not happen in parallel. To formalize this, we give a definition of data races in the thread-modular local trace semantics and show how exclusion criteria for potential conflicts can be expressed as digests. We report on our implementation of digest-driven data race detection in the static analyzer Goblint, and evaluate it on the SV-COMP benchmark suite. Combining the lockset digest with digests reasoning on thread ids and thread joins increases the number of correctly solved tasks by more than a factor of five compared to lockset reasoning alone.

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