The Seed is Strong: Seeding Strategies in Search-Based Software Testing

Search-based techniques have been shown useful for the task of generating tests, for example in the case of object-oriented software. But, as for any meta-heuristic search, the efficiency is heavily dependent on many different factors, seeding is one such factor that may strongly influence this efficiency. In this paper, we evaluate new and typical strategies to seed the initial population as well as to seed values introduced during the search when generating tests for object-oriented code. We report the results of a large empirical analysis carried out on 20 Java projects (for a total of 1,752 public classes). Our experiments show with strong statistical confidence that, even for a testing tool that is already able to achieve high coverage, the use of appropriate seeding strategies can further improve performance.