Automated path testing using the negative selection algorithm. (2017)

• Record Type: Journal Article
• Authors: Mohi-Aldeen, Shayma Mustafa; Mohamad, Radziah; Deris, Safaai
• Is Part Of: International journal of computational vision and robotics. Volume 7:Number 1/2(2017)
• Page Start: 160
• Page End: 171
• Publication Date: 2017
• Subjects: path testing -- automatic test data generation -- ATDG -- negative selection algorithm -- NSA -- software testing -- software development -- random testing -- genetic algorithms; Computer vision -- Periodicals; Robotics -- Periodicals; Artificial intelligence -- Periodicals; 006.3705
• Languages: English
• ISSNs: 1752-9131

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.