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Software testing can be re-formulated as a search problem, hence search algorithms (e.g., Genetic Algorithms) can be used to tackle it. Most of the research so far has been of empirical nature, in which novel proposed techniques have been validated on software testing benchmarks. However, only little attention has been spent to understand why meta-heuristics can be effective in software testing. This insight knowledge could be used to design novel more successful techniques. Recent theoretical work has tried to fill this gap, but it is very complex to carry out. This has limited its scope so far to only small problems. In this paper, we want to get insight knowledge on a difficult software testing problem. We combine together an empirical and theoretical analysis, and we exploit the benefits of both.
Andrea Arcuri (Wed,) studied this question.