Software testing consumes 50% of total software development cost. Test case design gains central importance in testing activity with respect to quality. The manual test suite generation is a time consuming and tedious task which needs automation. Unit testing is normally done in stringent time schedules by the developers or rarely by testers. In structural testing, it is not possible to check exhaustively all possible test data and the quality of test is dependent heavily on the performance of single developer or tester. Thus automation and optimization is required in generating test data to assist developer or tester with the selection of appropriate test data. A novel hybrid technique is developed to automate the test suite generation process for branch coverage criteria using evolutionary testing. The hybrid technique applies both Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to automatically generate test data. This technique improves efficiency and effectiveness of test case generation process when compared to applying Genetic Algorithm or Particle Swarm Optimization alone. The performance of proposed technique is evaluated and is observed that hybrid technique reduces the number of iterations by 47% when compared to GA and PSO applied separately and it reduces the execution time by 52% than GA and 48% than PSO.
Test Data Combination Strategy for Effective Test Suite Generation