Prioritizing test cases for regression techniques using metaheuristic techniques
Shweta Mittal, Om Prakash Sangwan · Journal of Information and Optimization Sciences · 2017
Regression testing attempts to find new errors after some alterations have been done in the software. Previously run test cases are re-executed to check whether historical faults have re-emerged or to validate the functionality of the software. In the past, several methods have been analyzed for reducing the cost of regression testing, which includes regression test selection, regression test minimization and regression test prioritization. Test Suite Prioritization techniques orders the test cases so that all the test cases are executed on a priority basis. Artificial bee colony (ABC) algorithm is a very powerful and efficient algorithm influenced by the intelligent nature of bees. In this paper, we have applied the ABC algorithm for test suite prioritization and the comparison has been made with Genetic Algorithm and Cuscuta Search. From our experimental results, we can infer that results of Cuscuta Search are low as compared to ABC and GA for small test suites.