Multi-objective Genetic Optimization for Noise-Based Testing of Concurrent Software

Hrubá, Vendula; Křena, Bohuslav; Letko, Zdeněk; Pluháčková, Hana; Vojnar, Tomáš

doi:10.1007/978-3-319-09940-8_8

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…Hrubá et al [21] and Avros et al [1] proposed methods to find suitable parameter values for random noise injection by using multiple-objective genetic algorithms and data mining. Fiedor et al [12] presented the experimental results of various existing heuristics and two new heuristics for the noise placement and noise seeding problems in terms of the detection capability, concurrency coverage, and performance degradation.…”

Section: Random Noise Injectionmentioning

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: Random Noise Injectionmentioning

confidence: 99%

Efficient noise injection for exposing hidden data races

Bae

2019

J Supercomput

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“…Hrubá et al (2012b) and Letko (2013) developed an approach using GA for noise injection application to concurrent programs for the run of new synchronization sequences and considered three coverage metrics, namely Synchro, Avio, and HBPair. Hrubá et al (2014) applied Multi-Objective Genetic Algorithm (NSGA-II), in which several metrics can be used as a fitness function. Poskitt and Poulding (2013) proposed contracts as the fitness function to be used while applying meta-heuristic techniques in concurrent programs in Eiffel.…”

Section: Search Based For Testing Concurrent Programsmentioning

confidence: 99%

“…The output of the SBBMuT approach is a set of synchronization sequences that can be used to support different testing approaches and techniques, such as mutation testing and structural testing. Arora et al (2015) classified approaches for testing concurrent programs in 8 categories, namely reachability testing (LEI; CARVER, 2010), structural testing (CARVER, 1993;TAYLOR et al, 1992;YANG et al, 1998;SOUZA et al, 2014), model-based testing, mutation testing (SILVA-BARRADAS, 1998;BRADBURY et al, 2006;SILVA et al, 2012b), slicing based testing, formal methods, random testing, and search based testing (HRUBÁ et al, 2014). Fig.…”

Section: Testing Executionmentioning

confidence: 99%

“…Some testing techniques defined for sequential programs have been adapted to be used for testing concurrent/distributed program, and tools have been developed to support those techniques. Arora et al (2015) classified approaches for testing concurrent programs in 8 categories, namely reachability testing (LEI; CARVER, 2010), structural testing (CARVER, 1993;TAYLOR et al, 1992;YANG et al, 1998;SOUZA et al, 2014), model-based testing (MCMILLAN, 1995;SHOUSHA et al, 2012), mutation testing (SILVA-BARRADAS, 1998;BRADBURY et al, 2006;SILVA et al, 2012b), slicing-based testing (CHENG, 1993;XU, 2001), formal methods (WEISS, 1988;VASENIN;KRIVCHIKOV, 2013), random testing (SEN, 2007;SEN, 2008), and search based testing (KŘENA et al, 2010;HRUBÁ et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Search based software testing for the generation of synchronization sequences for mutation testing of concurrent programs

Silva¹

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Firstly, I would like to thank God for the possibility to live every day with happiness and motivation. I would like to thank my family: my parents and to my brother and sister for supporting me throughout this PH.D. and all my life in general. Besides that, thank you for believing in my potential more than myself sometimes. I would like to express my sincere gratitude to my advisor Prof. Dr. Simone do Rocio Senger de Souza for the continuous support of my Ph.D. study, for her attention, availability, help and, mainly, for her patience. Her guidance helped me during this research and writing of this thesis. Also, I would like Prof. Dr. Paulo Sergio Lopes de Souza for the comments, encouragement, support and contribution to this research. My sincere thanks also go to Prof. Dr. Gordon Fraser who provided me an opportunity to join their team as an intern at the University of Sheffield, and who gave me access to the laboratory and research facilities. Thank you for your support and advice. His precious support during the internship was crucial for the results present in this thesis. I want to thank all the friends I made in Sheffield that helped me to feel more comfortable there and showed me how tasty is the meat pie at The Old House.

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Boosted decision trees for behaviour mining of concurrent programmes

Avros

Dudka

Křena

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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Multi-objective Genetic Optimization for Noise-Based Testing of Concurrent Software

Cited by 7 publications

References 26 publications

Efficient noise injection for exposing hidden data races

Efficient noise injection for exposing hidden data races

Search based software testing for the generation of synchronization sequences for mutation testing of concurrent programs

Boosted decision trees for behaviour mining of concurrent programmes

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