Particle Swarm Optimization Algorithm for Randomized Unit Testing
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Abstract
Randomized testing is an effective method for testing units of software. Thoroughness of randomized unit testing is according to the settings of optimal parameters. For the purpose of checking the test input data the randomized testing uses randomization. Designing Genetic algorithm is somewhat of a black art. The feature subset selection (FSS) tool is used with genetic algorithm to reduce the size and the content of the test case. The existing system does not cover all test cases because it can quickly generate many test cases and does not consider the target method. Thus GA for Randomized Unit Testing has not achieves high test coverage and does not produce better optimal test data. In this paper, particle swarm optimization (PSO) algorithm is used for randomized unit testing. PSO algorithm is used to evaluate the target method solutions for test coverage in test data. The main goal is to generate the optimal test parameter, reduce the size of test case and to achieve high coverage of the testing units. PSO achieves high coverage and produces the better optimal value within 20% of the time with better accuracy.
Keywords: Randomized unit testing, Genetic algorithm, Feature Sub Set Selection, PSO algorithm.
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