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Applying Genetic Evolutionary, Bacteriological and Quantum Evolutionary Algorithm for Improving Performance Optimization Segment of Test Data Sets in Mutation Testing Method
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Mohsen Fallah Rad*1, Sajad Bahrekazemi2
1, Department of Software Engineering, College of Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran.
2, M.Sc Student software engineering, Department of Computer Engineering, University of Guilan, Rasht, Iran and member of Young Researchers Club, Langeroud Branch, Islamic Azad University, Langeroud, Iran.
Email: 1mfalahrad@gmail.com, 2Sbahrekazemi@ymail.com
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Abstract
.Due to computer progress, computer systems became bigger and their function area expanded too. So, software testing, as a part of software engineering, has gained great importance. The goal of software testing is improving software quality and being sure about the accuracy of the final product; moreover the programmers test it for evaluating software accuracy. There are a variety of methods for software testing, among which, the mutation testing is the most famous one. In this method, high range mutants are made from the original program, and then attempts are made to discover mutants by the help of testing data collections. Whenever necessary, testing data can be improved or software deficiency can be found in the process of making and discovering mutant. This is done through algorithm of genetic evolutionary, bacteriological, particle swarm optimization, and evolutionary quantum, which have a high quality for research and can be done automatically. In these methods, test data can be improved by using the properties of the above evolutionary algorithms and without any human intervention in optimization part of the test data of mutation testing system, which consequently leads to a huge reduction in mutation testing costs. In these methods, test data can be improved by using the properties of the above evolutionary algorithms and without any human intervention in optimization part of the test data of mutation testing system, which consequently leads to a huge reduction in mutation testing costs. In this article, four methods of algorithm of genetic evolutionary, bacteriological, particle swarm optimization, and evolutionary quantum have been studied for improving testing data in mutation.
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Keywords
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software testing ; mutation testing ; genetic algorithm ; evolutionary quantum ; bacteriological algorithm
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URL: http://dx.doi.org/10.7321/jscse.v4.n1.2
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