Radial basis function neural network based approach to test oracle

Authors:
Om Prakash Sangwan;Pradeep Kumar Bhatia;Yogesh Singh
Affiliations:
Gautam Buddha University, Greater Noida, Uttar Pradesh, India;GJU of Science & Technology, Hisar, Haryana, India;M.S. University of Baroda, Gujarat, India
Venue:
ACM SIGSOFT Software Engineering Notes
Year:
2011

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Abstract

Software testing is an important discipline, and consumes significant amount of effort. A proper strategy is required to design and generate test cases systematically and effectively. In this paper automated software test case generation with Radial Basis Function Neural Network (RBFNN) has been proposed and empirically validated with the help of a case study and compared with other techniques of soft computing. Experimental results show that RBFNN is one of the best technique for automated test case generation.