Using Genetic Algorithms to Aid Test-Data Generation for Data-Flow Coverage

Ghiduk, Ahmed S.; Harrold, Mary Jean; Girgis, Moheb R.

doi:10.1109/aspec.2007.73

Cited by 50 publications

(49 citation statements)

References 11 publications

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“…This paper also introduces a fitness function by using dominance concept in control flow graph, elitism and branch distance. Results achieved using this approach are also compared with random testing and the results produced by the [8].…”

Section: A Ga In Structural Testingmentioning

confidence: 99%

Research on use of Nature Inspired Algorithms in Software Testing

Sharma¹,

Rizvi²,

Sharma³

2019

IJITEE

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The intention behind software testing is discovering defects in the developed software, making it error-free, robust and trustworthy. A large quantum of total efforts in the development of software is done on software testing. These efforts may include time, cost and most importantly manpower. The number of efforts required in software testing may be reduced if test data is generated automatically, without trade off the quality of the developed software. In literature, various nature inspired algorithms are used for the optimization of the software testing process. This article is a brief study on the applications of nature inspired algorithms in software testing. To keep the study succinct this study considers only two widely used testing types, structural and functional testing and literature available, since 2010 by considering only SCI or SCOPUS indexed publications.

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Section: A Ga In Structural Testingmentioning

confidence: 99%

Research on use of Nature Inspired Algorithms in Software Testing

Sharma¹,

Rizvi²,

Sharma³

2019

IJITEE

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show abstract

“…Step 4: Iterations add 1 and update the speed and position of test case according to the following formula (1), (2). Then produce the next generation of test case.…”

Section: B Steps Of Saf-gpso Test Generation Algorithmmentioning

confidence: 99%

“…Decreasing the test cost is very important to software development. Efficient test case is the key of software test process, therefore, domestic and foreign researchers have done a lot of research in recent years, and put forward many test case generation methods based on intelligent algorithm just like generation algorithm (GA) and particle swarm optimization (PSO) just like [1]- [2].By contrast, the latter got rapid development and achieved many results.…”

Section: Introductionmentioning

confidence: 99%

Software test cases generation based on improved particle swarm optimization

Huang

Zhang

2014

Proceedings of 2nd International Conference on Information Technology and Electronic Commerce

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The analysis of test case generation based on particle swarm algorithm introduced the group self-activity feedback (SAF) operator and Gauss mutation (G) changing inertia weight to improve the performance of particle swarm optimization (PSO). Using the improved algorithm in software test case, experiments show that the introduction of a single path fitness function structure and multi-path fitness calculation of parallel thinking are superior to the iteration time in single path test than standard PSO, and more efficient in multi-path test case generation.

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“…Hence There is a need to generate test data automatically to save the development time and cost. Some techniques are mentioned in the following references [12][13][14][15][16][17][18]. Although many meta heuristic approaches have been used to automate the process of test case generation successfully up to some extent, still there is a need of new meta heuristic algorithm which can minimize the overall time for test case generation and reduces the overall production cost.…”

Section: Real Life Application Of Ieammentioning

confidence: 99%

Improved environmental adaption method and its application in test case generation

Mishra

Tiwari

Misra

2014

Journal of Intelligent &Amp; Fuzzy Systems

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An Environmental Adaption Method (EAM) has been recently proposed in which, each solution updates its structure on the basis of current environmental fitness and its own fitness. EAM uses a new operator known as adaption operator. In this paper, an improved EAM, henceforth called as IEAM has been proposed and proper tuning of parameters of algorithm has been done to find the optimal solution in minimum time. IEAM has been tested on a set of benchmark functions. Results show the superiority of IEAM over PSO-TVAC, SADE and other optimization algorithms. Even for complex functions, the performance of IEAM is very good. In addition to this, IEAM has also been applied to software testing to generate test cases for white box testing.

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Using Genetic Algorithms to Aid Test-Data Generation for Data-Flow Coverage

Cited by 50 publications

References 11 publications

Research on use of Nature Inspired Algorithms in Software Testing

Research on use of Nature Inspired Algorithms in Software Testing

Software test cases generation based on improved particle swarm optimization

Improved environmental adaption method and its application in test case generation

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