Optimal test suite selection in regression testing with testcase prioritization using modified Ann and Whale optimization algorithm

Abstract

Fault detection during testing can provide faster feedback on the system under test and permit software engineers begin correcting faults earlier. One application of prioritization technique involves regression testing for retesting of software following modifications. In this context, prioritization technique can take advantage of information gathered about the previous execution of test cases to obtain test case orderings. Test case prioritization techniques schedule test cases in an order that increases their effectiveness in meeting certain performance goals. Regression testing makes sure that up gradation of software in terms of adding new features or for bug fixing purposes should not hamper previously working functionalities. Whenever a software is upgraded or modified, a set of test cases are run on each of its functions to assure that the change to that function is not affecting other parts of the software that were previously running flawlessly.Our proposed regression test case prioritization research initially generates test cases. Then the generated test cases are clustered with the aid of kernel fuzzy c-means clustering technique. The KFCM will cluster relevant and irrelevant test cases later the relevant test cases are considered for test case prioritization. The goal of test case prioritization is to determine test case ordering that maximizes the probability to discover faults in source code early. Here for test case prioritization Modified Artificial Neural Network classification algorithms are used. A Whale Optimization Algorithm is used for weight optimization process.