Advances in the ANaConDA framework for dynamic analysis and testing of concurrent C/C++ programs

Fiedor, Jan; Mužikovská, Monika; Smrčka, Aleš; Vašíček, Ondřej; Vojnar, Tomáš

doi:10.1145/3213846.3229505

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…The goal of random noise injection techniques [8,[13][14][15] is to disturb the normal thread interleaving by randomly delaying threads using the sleep, yield, and busy waiting mechanisms to expose hidden concurrency bugs. IBM's ConTest [8] is a framework used to statically inject noise into a specific Java bytecode or sourcecode location.…”

Section: Random Noise Injectionmentioning

confidence: 99%

“…Previous race detectors based on the random noise injection techniques [8,11,12,15,27] randomly disturb a normal program execution to find concurrency-related errors by probabilistically injecting several types of noise such as sleep, yield, and busy waiting before/after variable accesses or synchronization operations. This random noise injection technique is still used to test industrial code [13,37], but the high-strength frequent noise injection inevitably incurs high overheads for large-scale programs. Therefore, for large-scale programs that generate a large number of memory accesses and synchronizations, random noise injection may not be available because of its runtime overheads.…”

Section: Introductionmentioning

confidence: 99%

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Efficient noise injection for exposing hidden data races

Bae

2019

J Supercomput

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The happens-before (HB) relation is widely used to precisely detect data races, which are among the major causes of concurrency bugs. However, the HB relation is sensitive to thread interleaving; thus, hidden races that can be revealed in other thread interleavings may remain despite the repeated running of a program with an HB detector. In this paper, we propose a noise injection-based race detection technique (RaceInducer) that can expose and detect hidden races by using the runtime HB analysis result to determine the noise injection instants. RaceInducer delays a thread execution after the occurrence of the first no-lock non-racing accesses of three types (read-exclusive, write-exclusive, and read-shared) to a shared variable during a precise HB detection (FastTrack). Using this technique, RaceInducer can efficiently disturb the normal locking sequences between two threads that generate HB edges hiding data races. We implemented RaceInducer on the RoadRunner instrumentation framework for Java and evaluated its detection capability and runtime overheads on 12 real-world benchmark programs. The evaluation results showed that RaceInducer generated a lower runtime overhead compared to random noise injection that randomly injected noise for no-lock non-racing accesses and can detect more races with the same noise strength. RaceInducer generates particularly lower runtime overhead than the random noise injection for large programs that generates a high number of shared-memory accesses. In addition, we experimentally demonstrated that RaceInducer incurred lower runtime overheads than a current hidden-race detector (DrFinder) for programs with a relatively large number of locking operations.

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Section: Random Noise Injectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient noise injection for exposing hidden data races

Bae

2019

J Supercomput

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“…Related to the idea of CCT for bug-finding are noise-injection-based techniques [21,20,18]. These techniques rely on perturbing the execution of a concurrent program by injecting noise such as sleep statements, which force the execution to explore alternative interleavings.…”

Section: Related Workmentioning

confidence: 99%

“…However, the tradeoff is that the loss of control implies that the ability to explore specific interleavings, such as what PCT requires, is reduced. The ANaConDA tool has successfully demonstrated noise-injection in an industrial setting [21]. It can be interesting to explore the use of noise injection to provide coverage in portions of code that are not controlled by Coyote.…”

Section: Related Workmentioning

confidence: 99%

Industrial-Strength Controlled Concurrency Testing for $$\textsc {C}{} \texttt {\#} $$ Programs with $$\textsc {Coyote} $$

Deligiannis

Senthilnathan

Nayyar

et al. 2023

Lecture Notes in Computer Science

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This paper describes the design and implementation of the open-source tool $$\textsc {Coyote} $$ C O Y O T E for testing concurrent programs written in the $$\textsc {C}{} \texttt {\#} $$ C # language. $$\textsc {Coyote} $$ C O Y O T E provides algorithmic capabilities to explore the state-space of interleavings of a concurrent program, with deterministic repro for any bug that it finds. $$\textsc {Coyote} $$ C O Y O T E encapsulates multiple ideas from the research community to offer state-of-the-art testing for $$\textsc {C}{} \texttt {\#} $$ C # programs, as well as an efficiently engineered implementation that has been shown robust enough to support industrial use.

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