ABSTRACT The Species per Path Approach to SearchBased Test Data Generation

This paper introduces the Species per Path (SpP) approach to search-based software test data generation. The approach transforms the program under test into a version in which multiple paths to the search target are factored out. Test data are then sought for each individual path by dedicated ‘species ’ operating in parallel. The factoring out of paths results in several individual search landscapes, with feasible paths giving rise to landscapes that are shown to be more conducive to test data discovery than the original overall landscape. The paper presents the results of two empirical studies that validate and verify the approach. The validation study supports the claim that the approach is widely applicable and practical. The verification study shows that the SpP approach is likely to improve search efficiency and success rate over the standard evolutionary approach to test data generation