Testing of Concurrent Programs Using Genetic Algorithms

Hrubá, Vendula; Křena, Bohuslav; Letko, Zdeněk; Ur, Shmuel; Vojnar, Tomáš

doi:10.1007/978-3-642-33119-0_12

Cited by 9 publications

(11 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Here, a discussion on how a GA can be used to search for suitable types of noise heuristics and their parameters is provided as originally proposed by Hrubá et al (with some more details ). First, the task is formalized as the test and noise configuration search problem (the TNCS problem).…”

Section: Meta‐heuristic Setting Of Test and Noise Parametersmentioning

confidence: 99%

“…Compared with the described approach , the approach presented later does not search for concrete locations, which should be noised with particular noise. Instead, the approach searches for noise seeding and noise placement heuristics (or combinations of these heuristics) and their parameters, which can provide good results for a particular test and environment.…”

Section: Meta‐heuristic Setting Of Test and Noise Parametersmentioning

confidence: 99%

“…In this paper, a unified overview of multiple results from the area of noise‐injection‐based testing that were published by the authors in several past years is provided. In particular, multiple novel coverage metrics (first proposed by Křena et al ), which measure how well the behaviour of tested programmes has been examined from the point of view of possible occurrence of certain synchronization‐related errors, are presented first.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, multiple novel coverage metrics (first proposed by Křena et al ), which measure how well the behaviour of tested programmes has been examined from the point of view of possible occurrence of certain synchronization‐related errors, are presented first. These metrics can be used to control saturation‐based testing, to compare effectiveness of various testing approaches, or to tune parameters of meta‐heuristic algorithms applied in testing . Next, various noise‐injection heuristics and their influence on error detection and ability to increase concurrency‐related coverage (absolutely and relatively, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…The obtained experiences with noise injection on different levels (namely, Java bytecode and C/C++ binaries) are summarized into suggestions that can make further applications of noise‐based testing easier. Finally, an application of a meta‐heuristic algorithm (namely, the genetic algorithm) for an automatic selection of suitable combinations of values of the many parameters of tests and noise‐injection techniques is presented. The recent works of the authors unified in the paper are accompanied by a description of related works, and the paper can thus serve as a survey of the field of noise‐based testing of concurrent software too.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Advances in noise‐based testing of concurrent software

Fiedor

Hrubá

Křena

et al. 2014

Software Testing Verif & Rel

View full text Add to dashboard Cite

SummaryTesting of concurrent software written in programming languages like Java and C/C++ is a highly challenging task owing to the many possible interactions among threads. A simple, cheap, and effective approach that addresses this challenge is testing with noise injection, which influences the scheduling so that different interleavings of concurrent actions are witnessed. In this paper, multiple results achieved recently in the area of noise‐injection‐based testing by the authors are presented in a unified and extended way. In particular, various concurrency coverage metrics are presented first. Then, multiple heuristics for solving the noise placement problem (i.e. where and when to generate noise) as well as the noise seeding problem (i.e. how to generate the noise) are introduced and experimentally evaluated. In addition, several new heuristics are proposed and included into the evaluation too. Recommendations on how to set up noise‐based testing for particular scenarios are then given. Finally, a novel use of the genetic algorithm for finding suitable combinations of the many parameters of tests and noise techniques is presented. Copyright © 2014 John Wiley & Sons, Ltd.

show abstract

Section: Meta‐heuristic Setting Of Test and Noise Parametersmentioning

confidence: 99%

Section: Meta‐heuristic Setting Of Test and Noise Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Advances in noise‐based testing of concurrent software

Fiedor

Hrubá

Křena

et al. 2014

Software Testing Verif & Rel

View full text Add to dashboard Cite

show abstract

A systematic review of approaches for testing concurrent programs

Arora

Bhatia

Singh

2015

Concurrency and Computation

View full text Add to dashboard Cite

SUMMARYConcurrent programs are replacing the sequential programs as they utilize the true capabilities of multicore architecture. The extensive use of multicore systems and multithreaded paradigms warrants more attention to the testing of the concurrent programs. The testing concurrent program is not a new field as it has been more than 40 years because the first problem related to the testing concurrent program was addressed by the researchers. The field covers various domains, which include concurrency problems, testing approaches, techniques, graphical representations, tools, and subject systems. This paper aims at providing an overview of research in the domain of testing concurrent programs by classifying it into eight categories: (a) reachability testing, (b) structural testing, (c) model-based testing, (d) mutation-based testing, (e) slicing-based testing, (f) formal methods, (g) random testing, and (h) search-based testing. The survey is focused on the techniques applied, methodologies followed, and tools used in these aforementioned approaches. Furthermore, the gaps are also identified in different approaches. The paper concludes with the consolidation of various testing parameters along with the future directions.

show abstract

Boosted decision trees for behaviour mining of concurrent programmes

Avros

Dudka

Křena

et al. 2017

Concurrency and Computation

View full text Add to dashboard Cite

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).

show abstract

Testing of Concurrent Programs Using Genetic Algorithms

Cited by 9 publications

References 17 publications

Advances in noise‐based testing of concurrent software

Advances in noise‐based testing of concurrent software

A systematic review of approaches for testing concurrent programs

Boosted decision trees for behaviour mining of concurrent programmes

Contact Info

Product

Resources

About