Automated path testing using the negative selection algorithm
by Shayma Mustafa Mohi-Aldeen; Radziah Mohamad; Safaai Deris
International Journal of Computational Vision and Robotics (IJCVR), Vol. 7, No. 1/2, 2017

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.

Online publication date: Sun, 01-Jan-2017

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