Petri Nets-based Methods on Automatically Detecting for Concurrency Bugs in Rust Programs
December 06, 2022 Β· Declared Dead Β· + Add venue
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Kaiwen Zhang, Guanjun Liu
arXiv ID
2212.02754
Category
cs.PL: Programming Languages
Cross-listed
cs.FL,
cs.SE
Citations
0
Last Checked
4 months ago
Abstract
Rust's memory safety guarantees, notably ownership and lifetime systems, have driven its widespread adoption. Concurrency bugs still occur in Rust programs, and existing detection approaches exhibit significant limitations: static analyzers suffer from context insensitivity and high false positives, while dynamic methods incur prohibitive runtime costs due to exponential path exploration. This paper presents a Petri net-based method for efficient, precise detection of Rust concurrency bugs. The method rests on three pillars: (1) A syntax-preserving program-to-Petri-net transformation tailored for target bug classes; (2) Semantics-preserving state compression via context-aware slicing; (3) Bug detection through efficient Petri net reachability analysis. The core innovation is its rigorous, control-flow-driven modeling of Rust's ownership semantics and synchronization primitives within the Petri net structure, with data operations represented as token movements. Integrated pointer analysis automates alias identification during transformation. Experiments on standard Rust concurrency benchmarks demonstrate that our method outperforms the state-of-the-art methods LockBud and Miri that are both tools of detecting concurrency bugs of Rust programs. Compared to LockBud, our approach reduces false positives by 35.7\% and false negatives by 28.3\% , which is obtained through our precise flow-sensitive pointer analysis. Compared with Miri that is a dynamic analysis tool, although Miri can obtain the same detection results, our method achieves 100% faster verification speed since our method takes a state reduce algorithm.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Programming Languages
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
π»
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
π»
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
π»
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
π»
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted