Title: Automated path testing using the negative selection algorithm

Authors: Shayma Mustafa Mohi-Aldeen; Radziah Mohamad; Safaai Deris

Addresses: Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia; College of Computer Science and Mathematics, University of Mosul, 41002, Mosul, Iraq ' Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia ' Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia

Abstract: Software testing is an important step in the software development process, accounting for more than 50% of software development cost as it is laborious and time-consuming. Generating path test data is the most critical stage in software testing and many approaches have been developed by researchers to automate it. Negative selection algorithm (NSA) has been used in this paper to generate test data for path testing automatically. The proposed algorithm has been applied to the most commonly used benchmarking program which is triangle classifier. The experimental results show that the proposed algorithm is more efficient in time of execution and more effective in the generation of test data when compared with random testing and genetic algorithm.

Keywords: path testing; automatic test data generation; ATDG; negative selection algorithm; NSA; software testing; software development; random testing; genetic algorithms.

DOI: 10.1504/IJCVR.2017.081236

International Journal of Computational Vision and Robotics, 2017 Vol.7 No.1/2, pp.160 - 171

Received: 29 Nov 2014
Accepted: 08 Apr 2015

Published online: 07 Dec 2016 *

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