Evolutionary Testing Approach for Solving Path- Oriented Multivariate Problems

A multivariate approach involves varying number of objectives to be satisfied simultaneously in testing process. An evolutionary approach, genetic algorithm is taken for solving multivariate problems in software engineering. The Multivariate Optimization Problem (MOP) has a set of solutions, each of which satisfies the objectives at an acceptable level. Another evolutionary algorithm named SBGA (stagebased genetic algorithm) with two stages is attempted for solving problems with multiple objectives like cost minimization, time reduction and maximizing early fault deduction capabilities. In this paper, a multivariate genetic algorithm (MGA) in terms of stages for path-based programs is presented to get the benefits of both multi-criteria optimization and genetic algorithm. The multiple variables considered for test data generation are maximum path coverage with minimum execution time and test-suite minimization. The path coverage and the no. of test cases generated using SBGA are experimented with low, medium and high complexity object-oriented programs and compared with the existing GA approaches. The data-flow testing of OOPs in terms of path coverage are resulted with almost 88%. Thus, the efficiency of generated testcases has been improved in terms of path coverage with minimum execution time as well as with the minimal test suite size.