A hybrid-adaptive neuro-fuzzy inference system for multi-objective regression test suites optimization

Abstract

Regression testing is a mandatory activity of software development life cycle, which is performed to ensure that modifications have not caused any adverse effects on the system’s functionality. With every change in software in the maintenance phase, the size of regression test suite grows as new test cases are written to validate changes. The bigger size of regression test suite makes the testing expensive and time-consuming. Optimization of regression test suite is a possible solution to cope with this problem. Various techniques of optimization have been proposed; however, there is no perfect solution for the problem and therefore, requires better solutions to improve the optimization process. This paper presents a novel technique named as hybrid-adaptive neuro-fuzzy inference system tuned with genetic algorithm and particle swarm optimization algorithm that is used to optimize the regression test suites. Evaluation of the proposed approach is performed on benchmark test suites including “previous date problem” and “Siemens print token.” Experimental results are compared with existing state-of-the-art techniques, and results show that the proposed approach is more effective for the reduction in a regression test suites with higher requirement coverage. The size of regression test suites can be reduced up to 48% using the proposed approach without reducing the fault detection rate.