Noise Injection Heuristics for Concurrency Testing

Bohuslav, K; ena,; Letko, Zdeněk; Tom,; Vojnar, Tomáš

doi:10.1007/978-3-642-25929-6_12

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…A lightweight alternative to systematic thread control and active testing is random noise injection [8]. 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.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

Efficient noise injection for exposing hidden data races

Bae

2019

J Supercomput

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“…Existem na literatura alguns trabalhos (Eytani, 2006;Hrubá et al, 2012;Krena et al, 2011;Letko, 2010) que usam meta-heurísticas para inserção de defeitos para teste em programas concorrentes. Como a proposta deste mestradoé sobre geração de dados, esses trabalhos não são detalhados nessa seção.…”

Section: Considerações Iniciaisunclassified

“…O Teste Baseado em Buscaé o processo de gerar automaticamente dados de teste de acordo com um critério de adequação (função de aptidão) usando algoritmos de otimização baseado em busca queé guiado por uma função de avaliação (Harman e McMinn, 2010). Essas técnicas aplicadas para o contexto de programas sequenciais (Bueno et al, 2001;Hrubá et al, 2012;Krena et al, 2011;Letko, 2010), apresentam resultados motivadores. Entretanto, poucos trabalhos exploram a geração de dados para o contexto de programas concorrentes.…”

Section: Introductionunclassified

Geração automática de dados de teste para programas concorrrentes com meta-heurística

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Boosted decision trees for behaviour mining of concurrent programmes

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et al. 2017

Concurrency and Computation

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SummaryTesting of concurrent programmes is difficult since the scheduling nondeterminism requires one to test a huge number of different thread interleavings. Moreover, repeated test executions that are performed in the same environment will typically examine similar interleavings only. One possible way how to deal with this problem is to use the noise injection approach, which influences the scheduling by injecting various kinds of noise (delays, context switches, etc) into the common thread behaviour. However, for noise injection to be efficient, one has to choose suitable noise injection heuristics from among the many existing ones as well as to suitably choose values of their various parameters, which is not easy. In this paper, we propose a novel way how to deal with the problem of choosing suitable noise injection heuristics and suitable values of their parameters (as well as suitable values of parameters of the programmes being tested themselves). Here, by suitable, we mean such settings that maximize chances of meeting a given testing goal (such as, eg, maximizing coverage of rare behaviours and thus maximizing chances to find rarely occurring concurrency‐related bugs). Our approach is, in particular, based on using data mining in the context of noise‐based testing to get more insight about the importance of the different heuristics in a particular testing context as well as to improve fully automated noise‐based testing (in combination with both random as well as genetically optimized noise setting).

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Noise Injection Heuristics for Concurrency Testing

Cited by 6 publications

References 11 publications

Efficient noise injection for exposing hidden data races

Efficient noise injection for exposing hidden data races

Geração automática de dados de teste para programas concorrrentes com meta-heurística

Boosted decision trees for behaviour mining of concurrent programmes

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