Regression Test Case Optimization Using Honey Bee Mating Optimization Algorithm with Fuzzy Rule Base

Maintenance of the software is concerned with the changes and modifications implemented to the software. It needs to be validated that the modifications has not led to the degradation in the quality of the software. Software regression testing is required to instill confidence that changes are valid. Regression testing is very expensive. It requires the optimization of regression test cases. Recently, evolutionary and metaheuristic algorithms are coming into existence as search and optimization tools. This paper proposes a regression test case optimization technique based on honey bee mating optimization and fuzzy rule base, which reduces the size of the test suite by selecting the test cases from the existing test suite. The test cases which are necessary for validating the recent changes in the software and have the ability to find the faults and cover maximum coding under testing in minimum time are selected. An algorithm is designed which takes as input the test suite containing several test cases and based on their execution time and fault coverage, reduces the number of test cases. The test suite is reduced by 50% which optimizes the overall regression testing process. The proposed algorithm reduces the test suite data by approximately 50%. Although the percentage reduction is found little bit low in comparison to existing algorithm but the overall algorithm is found efficient as it provides an authentic way of selecting test cases using fuzzy rule base.