Software Analysis Techniques for Detecting Data Race

Kang, Pilsung

doi:10.1587/transinf.2017edr0004

Cited by 4 publications

(3 citation statements)

References 58 publications

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“…Dynamic analysis gets more popularity in many applications, such as data race detection [12], [26]- [28], atomicity violation detection [29]- [31], record and replay systems [32]- [35]. Researchers have surveyed data race detection techniques from various views [36]- [38]. Generally, data race detection techniques are based on two models: locksets and happens-before relation.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Data Race Detection Based on Thread Sharing Analysis With Different Granularities: An Empirical Study

Jiang

Qian

et al. 2019

IEEE Access

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Thread Sharing Analysis (TSA) plays an important role in concurrent program testing. Providing a TSA to a data race detector may speed up the runtime logging and improve the performance of data race detection. In this paper, we focus on the empirical study of the performance of data race detection based on TSA with different granularities. First, three granularities are considered, including object, field and ''field + array element''. Then, an empirical study is conducted to evaluate the performance of data race detection based on three dynamic TSA approaches. The results show that data race detection based on the TSA with ''field + array element'' granularity outperforms those with object and field granularities. INDEX TERMS Software testing, concurrent program testing, thread sharing analysis (TSA), data race detection, dynamic analysis

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Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Data Race Detection Based on Thread Sharing Analysis With Different Granularities: An Empirical Study

Jiang

Qian

et al. 2019

IEEE Access

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“…Numerous works were conducted on detecting data races based on dynamic or static approaches [2] in the past few years. Dynamic detection examined the behavior of the source code by collecting variables and aliases at runtime [3].…”

Section: Introductionmentioning

confidence: 99%

Context-Sensitive Data Race Detection for Concurrent Programs

2021

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Data races have become one of the severe issues affecting the correctness of concurrent programs. Existing detection tools for data races have many false positives and false negatives. To improve the correctness, this paper proposes a context-sensitive approach to detect data races in concurrent programs. Several program analysis techniques are conducted to reduce false positives and false negatives. First, control-flow analysis is used to construct call graphs, in which escape analysis is employed to find escaped objects among threads. Second, context-sensitive alias analysis is employed to find alias objects. Finally, happens-before analysis is conducted to reduce false positives and false negatives. We develop a prototype tool ConRacer based on WALA and evaluate it on several benchmarks. We also compare ConRacer with existing static and dynamic tools. The experimental results show that ConRacer can effectively detect data races and reduce false positives and false negatives.

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“…Previous studies have proposed a lot of approaches to expose and detect all kinds of concurrency bugs [29], such as deadlocks [30,31], data races [32,33], atomicity violations [34,35], and order violations [36]. Also, the studies have obtained many excellent results.…”

Section: Introductionmentioning

confidence: 99%

A Constraint-Aware Optimization Method for Concurrency Bug Diagnosis Service in a Distributed Cloud Environment

Jiang

2018

Security and Communication Networks

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The advent of cloud computation and big data applications has enabled data access concurrency to be prevalent in the distributed cloud environment. In the meantime, security issue becomes a critical problem for researchers to consider. Concurrency bug diagnosis service is to analyze concurrent software and then reason about concurrency bugs in them. However, frequent context switches in concurrent program execution traces will inevitably impact the service performance. To optimize the service performance, this paper presents a static constraint-aware method to simplify concurrent program buggy traces. First, taking the original buggy trace as the operation object, we calculate the maximal sound dependence relations based on the constraint models. Then, we iteratively check the dependent constraints and move forward current event to extend thread execution intervals. Finally, we obtain the simplified trace that is equivalent to the original buggy trace. To evaluate our approach, we conduct a set of experiments on 12 widely used Java projects. Experimental results show that our approach outperforms other state-of-the-art approaches in terms of execution time.

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Software Analysis Techniques for Detecting Data Race

Cited by 4 publications

References 58 publications

Performance Evaluation of Data Race Detection Based on Thread Sharing Analysis With Different Granularities: An Empirical Study

Performance Evaluation of Data Race Detection Based on Thread Sharing Analysis With Different Granularities: An Empirical Study

Context-Sensitive Data Race Detection for Concurrent Programs

A Constraint-Aware Optimization Method for Concurrency Bug Diagnosis Service in a Distributed Cloud Environment

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