Weighted Particle Swarm Optimization algorithm for test data generation

Recently, Search Based Software Testing (SBST) research has gained much attention in producing the optimal solution for the optimization problem by automating the test data generation for the branch coverage criterion. Particle Swarm Optimization (PSO) has been emerged for obtaining optimal solution...

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Bibliographic Details
Published in2016 International Conference on Computing, Analytics and Security Trends (CAST) pp. 35 - 39
Main Authors Gopi, Pooja, Ramalingam, Mohanasundari, Maruthaperumal, Anand Kumar, Panakkal, Sayooj, Murugan, Jayashri, Arumugam, Chamundeswari
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2016
Subjects
Branch coverage
Convergence
Fitness function
Mathematical model
Particle swarm optimization
SBST
Sociology
Software
Statistics
Testing
Weighted Particle Swarm Optimization
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Summary:Recently, Search Based Software Testing (SBST) research has gained much attention in producing the optimal solution for the optimization problem by automating the test data generation for the branch coverage criterion. Particle Swarm Optimization (PSO) has been emerged for obtaining optimal solution for the test data generation problem because of its easy implementation, fast convergence and few parameters. An attempt has been made in this paper to apply a weight based swarm intelligence algorithm called Weighted Particle Swarm Optimization (WPSO) to automate the test data generation for the branch coverage criterion. A fitness function including weight parameter is proposed to use in WPSO algorithm for evaluating the fitness using minimization approach. This fitness function aims to achieve high coverage and fast convergence. A benchmark program is used for the experimental analysis to compare the high coverage and fast convergence obtained from WPSO and PSO algorithms.
DOI:10.1109/CAST.2016.7914936