An Improved Technique of Fitness Evaluation for Evolutionary Testing

Many Search Based Software testing (SBST) have been proposed and experiments show that they can generate effective test data. However, a meta-heuristic search (MHS) algorithm in these techniques incurs considerable computation cost to evaluate fitness values, which results in huge test case generation cost. In this paper, we propose a more effective fitness evaluation technique based on Fitness Evaluation Program (FEP). FEP, derived from a path constraint of SUT, is introduced as a special program for evaluating fitness values. We implement a test generation tool, named ConGA, and apply it to generate test cases for C programs for evaluating efficiency of the FEP-based test case generation technique. The experiments show that the proposed technique can reduce significant amount of test data generation time on average.