Efficient concurrency-bug detection across inputs

Deng, Dongdong; Zhang, Wei; Lu, Shan

doi:10.1145/2509136.2509539

Cited by 23 publications

(6 citation statements)

References 74 publications

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“…There are techniques for ensuring safe programming using advanced typing [5], analyses for static race detection [44], approaches to concurrency bug fixing [32], tools that flag suspicious concurrency patterns [30,50], and much more. Additionally, dynamic techniques for concurrency bug detection [1,13,34,41,46] often benefit from symbolic reasoning, especially in approaches inspired by model checking or symbolic execution techniques [33,37,43]. Such past work is only superficially related to ours, since the aims, programming abstractions, and analysis techniques used are quite different.…”

Section: Related Workmentioning

confidence: 99%

Symbolic reasoning for automatic signal placement

Ferles

Geffen

Dillig

et al. 2018

Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation

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Explicit signaling between threads is a perennial cause of bugs in concurrent programs. While there are several runtime techniques to automatically notify threads upon the availability of some shared resource, such techniques are not widely-adopted due to their run-time overhead. This paper proposes a new solution based on static analysis for automatically generating a performant explicit-signal program from its corresponding implicit-signal implementation. The key idea is to generate verification conditions that allow us to minimize the number of required signals and unnecessary context switches, while guaranteeing semantic equivalence between the source and target programs. We have implemented our method in a tool called Expresso and evaluate it on challenging benchmarks from prior papers and open-source software. Expresso-generated code significantly outperforms past automatic signaling mechanisms (avg. 1.56x speedup) and closely matches the performance of hand-optimized explicit-signal code. CCS Concepts • Software and its engineering → Concurrent programming languages; Concurrent programming structures;

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Section: Related Workmentioning

confidence: 99%

Symbolic reasoning for automatic signal placement

Ferles

Geffen

Dillig

et al. 2018

Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation

View full text Add to dashboard Cite

show abstract

“…There are techniques for ensuring safe programming using advanced typing [5], analyses for static race detection [43], approaches to concurrency bug fixing [31], tools that flag suspicious concurrency patterns [29,49], and much more. Additionally, dynamic techniques for concurrency bug detection [1,13,33,40,45] often benefit from symbolic reasoning, especially in approaches inspired by model checking or symbolic execution techniques [32,36,42]. Such past work is only superficially related to ours, since the aims, programming abstractions, and analysis techniques used are quite different.…”

Section: Related Workmentioning

confidence: 99%

“…First, we assume that predicates appearing in waituntil statements are expressible in some first-order theory (e.g., linear arithmetic). Hence, if the predicate corresponds to the result of a complicated function13…”

mentioning

confidence: 99%

Symbolic reasoning for automatic signal placement

et al. 2018

View full text Add to dashboard Cite

Explicit signaling between threads is a perennial cause of bugs in concurrent programs. While there are several runtime techniques to automatically notify threads upon the availability of some shared resource, such techniques are not widely-adopted due to their run-time overhead. This paper proposes a new solution based on static analysis for automatically generating a performant explicit-signal program from its corresponding implicit-signal implementation.The key idea is to generate verification conditions that allow us to minimize the number of required signals and unnecessary context switches, while guaranteeing semantic equivalence between the source and target programs. We have implemented our method in a tool called Expresso and evaluate it on challenging benchmarks from prior papers and open-source software. Expresso-generated code significantly outperforms past automatic signaling mechanisms (avg. 1.56x speedup) and closely matches the performance of hand-optimized explicit-signal code.

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“…Testing usually requires exploring as many interleavings as possible to amplify the chance of exposing faults. Recent work [22] reports that testing concurrent programs can introduce a 10x-100x slowdown for each test run. Such overhead increases as test suite size increases.…”

Section: Introductionmentioning

confidence: 99%

ConPredictor: Concurrency Defect Prediction in Real-World Applications

Wen

Han

et al. 2019

IIEEE Trans. Software Eng.

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Concurrent programs are difficult to test due to their inherent non-determinism. To address this problem, testing often requires the exploration of thread schedules of a program; this can be time-consuming when applied to real-world programs. Software defect prediction has been used to help developers find faults and prioritize their testing efforts. Prior studies have used machine learning to build such predicting models based on designed features that encode the characteristics of programs. However, research has focused on sequential programs; to date, no work has considered defect prediction for concurrent programs, with program characteristics distinguished from sequential programs. In this paper, we present ConPredictor, an approach to predict defects specific to concurrent programs by combining both static and dynamic program metrics. Specifically, we propose a set of novel static code metrics based on the unique properties of concurrent programs. We also leverage additional guidance from dynamic metrics constructed based on mutation analysis. Our evaluation on four large open source projects shows that ConPredictor improved both within-project defect prediction and cross-project defect prediction compared to traditional features.

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Efficient concurrency-bug detection across inputs

Cited by 23 publications

References 74 publications

Symbolic reasoning for automatic signal placement

Symbolic reasoning for automatic signal placement

Symbolic reasoning for automatic signal placement

ConPredictor: Concurrency Defect Prediction in Real-World Applications

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