Beyond Locks and Thread IDs: Static Data Race Detection Off The Beaten Path (Extended Version)

• URL: http://arxiv.org/abs/2609.00246v1
• Date: Mon, 31 Aug 2026 18:47:23 GMT
• Title: Beyond Locks and Thread IDs: Static Data Race Detection Off The Beaten Path (Extended Version)
• Abstract summary: We extend the digest framework to handle constructs and synchronization mechanisms that have been ignored in static race detection.<n>We introduce mechanisms for the commonly used thread barriers, as well as pthread_once, which allows to ensure that an action is executed only once.
• Score: 3.5832940717897217
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Maintaining an abstraction of the execution history of threads can improve the precision of data race detection in static analysis. Here, we extend the digest framework to handle concurrency constructs and synchronization mechanisms that have been ignored in static race detection. We introduce mechanisms for the commonly used thread barriers, as well as pthread_once, which allows to ensure that an action is executed only once. We also instantiate the framework with an abstraction of locksets held by ancestor threads. We propose a suite of litmus tests to evaluate analyses for these features and compare our implementation to state-of-the-art tools, finding that they lack support.

Related papers

• SoK: Intent-Oriented Systematization of Multi-Turn LLM Jailbreaks [69.08780911753102]
  Multi-turn jailbreaks exploit a pattern by advancing a harmful intent across turns, so that no single message exposes the full objective.<n>We develop a four-part, intent-oriented taxonomy that organizes multi-turn jailbreaks by adversarial intent structure.
  arXiv  Detail & Related papers  (2026-08-02T09:24:08Z)
• The Path Matters: Learning a Token-Commitment Policy for Diffusion Language Models [52.93186090124315]
  We argue that token commitment can be learned as a reusable trace-state policy.<n>We introduce TraceLock, a lightweight plug-in controller that instantiates this policy for a frozen diffusion language model.
  arXiv  Detail & Related papers  (2026-05-23T18:23:46Z)
• Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy [51.758416625168]
  Randomized smoothing is a powerful tool for certifying robustness to adversarial perturbations.<n>We propose a framework for certification of complex, composed mechanisms.<n>We provide a principled and general framework for using composite mechanisms to certify robustness under complex threat models.
  arXiv  Detail & Related papers  (2026-05-20T22:17:29Z)
• Memory Forensics Techniques for Automated Detection and Analysis of Go Malware [0.73493417424903]
  We present the first memory forensics framework for runtime analysis of Go binaries.<n>Our framework reconstructs type and function metadata, recovers heap-allocated and static strings, and distinguishes application-level functions.
  arXiv  Detail & Related papers  (2026-05-13T18:34:00Z)
• Dynamic analysis enhances issue resolution [53.50448142467294]
  DAIRA (Dynamic Analysis-enhanced Issue Resolution Agent) is an automated repair framework that embeds dynamic analysis into the agent's reasoning cycle.<n> Driven by a Test Tracing-Driven methodology, DAIRA utilizes lightweight monitors to extract critical runtime data.<n>When powered by Gemini 3 Flash Preview, DAIRA establishes a new state-of-the-art (SOTA) performance, achieving a 79.4% resolution rate on the SWE-bench Verified dataset.
  arXiv  Detail & Related papers  (2026-03-23T14:48:54Z)
• Compiling Away the Overhead of Race Detection [4.072903728718951]
  Dynamic data race detectors are indispensable for flagging errors in software, but their high runtime overhead limits their adoption.<n>We introduce a suite of interprocedural static analyses to eliminate instrumentation for provably race-free accesses.<n>Our approach significantly reduces race detection overhead, achieving a geomean speedup of 1.34x, with peak speedups reaching 2.5x under high thread contention.
  arXiv  Detail & Related papers  (2025-12-05T09:26:08Z)
• Static Deadlock Detection for Rust Programs [6.596623081054982]
  Rust relies on its unique ownership mechanism to ensure thread and memory safety. New language features in Rust pose new challenges for vulnerability detection. This paper proposes a static deadlock detection method tailored for Rust programs.
  arXiv  Detail & Related papers  (2024-01-02T09:09:48Z)
• Object-Centric Conformance Alignments with Synchronization (Extended Version) [57.76661079749309]
  We present a new formalism that combines the ability of object-centric Petri nets to capture one-to-many relations and the one of Petri nets with identifiers to compare and synchronize objects based on their identity. We propose a conformance checking approach for such nets based on an encoding in satisfiability modulo theories (SMT)
  arXiv  Detail & Related papers  (2023-12-13T21:53:32Z)
• Seneca: Taint-Based Call Graph Construction for Java Object Deserialization [3.6731536660959985]
  We present Seneca, an approach for handling serialization with improved soundness in the context of call graph construction. We evaluate our approach with respect to soundness, precision, performance, and usefulness in detecting untrusted object deserialization vulnerabilities.
  arXiv  Detail & Related papers  (2023-11-02T02:07:54Z)
• Petri Nets-based Methods on Automatically Detecting for Concurrency Bugs in Rust Programs [5.064868362565062]
  This paper presents a Petri net-based method for efficient, precise detection of Rust bugs.<n>The core innovation is its rigorous, control-flow-driven modeling of Rust's ownership semantics and synchronization primitives.<n>Compared to LockBud, our approach reduces false positives by 35.7% and false negatives by 28.3%.
  arXiv  Detail & Related papers  (2022-12-06T05:11:21Z)
• FineDiving: A Fine-grained Dataset for Procedure-aware Action Quality Assessment [93.09267863425492]
  We argue that understanding both high-level semantics and internal temporal structures of actions in competitive sports videos is the key to making predictions accurate and interpretable. We construct a new fine-grained dataset, called FineDiving, developed on diverse diving events with detailed annotations on action procedures.
  arXiv  Detail & Related papers  (2022-04-07T17:59:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.