Eccentric Test Data Generation for Path Testing Using Genetic Algorithm

Effective and efficient test data generation is one of the major challenging and time-consuming tasks within the software testing process. Researchers have proposed different methods to generate test data automatically; however, those methods suffer from different drawbacks. In this paper, the authors present a genetic algorithm-based approach that tries to generate a test data that is expected to cover a given set of target paths. Their proposed fitness function is intended to achieve path coverage that incorporates path traversal techniques, neighborhood influence, weighting, and normalization.