# Automated detection of atomicity violations in large-scale systems

arXiv: https://arxiv.org/abs/2504.00521 · HTML: https://mlsanity.com/paper/2504.00521/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 68 references that are in the map.
- First posted on arXiv 1 Apr 2025.

## Builds on (most cited first, 15 of 15)

- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges](https://mlsanity.com/paper/2401.07339/) (2024)
- [Large Language Model-Based Agents for Software Engineering: A Survey](https://mlsanity.com/paper/2409.02977/) (2024)
- [CodeR: Issue Resolving with Multi-Agent and Task Graphs](https://mlsanity.com/paper/2406.01304/) (2024)
- [MarsCode Agent: AI-native Automated Bug Fixing](https://mlsanity.com/paper/2409.00899/) (2024)
- [Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG](https://mlsanity.com/paper/2406.11147/) (2024)
- [AgileCoder: Dynamic Collaborative Agents for Software Development based on Agile Methodology](https://mlsanity.com/paper/2406.11912/) (2024)
- [CodeAgent: Autonomous Communicative Agents for Code Review](https://mlsanity.com/paper/2402.02172/) (2024)
- [Multi-role Consensus through LLMs Discussions for Vulnerability Detection](https://mlsanity.com/paper/2403.14274/) (2024)
- [A Contemporary Survey of Large Language Model Assisted Program Analysis](https://mlsanity.com/paper/2502.18474/) (2025)
- [E&V: Prompting Large Language Models to Perform Static Analysis by Pseudo-code Execution and Verification](https://mlsanity.com/paper/2312.08477/) (2023)
- [Enhancing Automated Program Repair with Solution Design](https://mlsanity.com/paper/2408.12056/) (2024)
- [Modular Verification of Interrupt-Driven Software](https://mlsanity.com/paper/1709.10078/) (2017)
- [Let's Ask AI About Their Programs: Exploring ChatGPT's Answers To Program Comprehension Questions](https://mlsanity.com/paper/2404.11734/) (2024)
- [LLVM Static Analysis for Program Characterization and Memory Reuse Profile Estimation](https://mlsanity.com/paper/2311.12883/) (2023)

## Built on by (most cited first, 1 of 1)

- [CheckerBench: Can Long-Horizon Agents Synthesize Static-Analysis Checkers?](https://mlsanity.com/paper/2610.07557/) (2026)

## Abstract (arXiv)

Atomicity violations in interrupt-driven programs pose a significant threat to software reliability in safety-critical systems. These violations occur when the execution sequence of operations on shared resources is disrupted by asynchronous interrupts. Detecting atomicity violations is challenging due to the vast program state space, application-level code dependencies, and complex domain-specific knowledge. In this paper, we propose CLOVER, a multi-agent framework for detecting atomicity violations in real-world interrupt-driven programs. Its plan agent orchestrates four static analysis tools to extract key information and generate code summaries. CLOVER then initializes several Expert-Judge agent pairs to detect and validate different patterns of atomicity violation, through an iterative manner. Evaluations on RaceBench, SV-COMP, and RWIP demonstrate that CLOVER achieves a precision/recall of 91.0%/96.4%, outperforming existing approaches by 33.0-117.2% on F1-score. Additionally, it identifies 12 atomicity violations in 11 real-world aerospace software projects, one of which is previously unknown.