An Empirical Evaluation of Evolutionary Algorithms for Unit Test Suite Generation

peer reviewedContext: Evolutionary algorithms have been shown to be e ective at generating unit test suites optimised for code coverage. While many speci c aspects of these algorithms have been evaluated in detail (e.g., test length and di erent kinds of techniques aimed at improving performance, like seeding), the in uence of the choice of evolutionary algorithm has to date seen less attention in the literature. Objective: Since it is theoretically impossible to design an algorithm that is the best on all possible problems, a common approach in software engineering problems is to rst try the most common algorithm, a Genetic Algorithm, and only afterwards try to re ne it or compare it with other algorithms to see if any of them is more suited for the addressed problem. The objective of this paper is to perform this analysis, in order to shed light on the in uence of the search algorithm applied for unit test generation. Method: We empirically evaluate thirteen di erent evolutionary algorithms and two random approaches on a selection of non-trivial open source classes. All algorithms are implemented in the EvoSuite test generation tool, which includes recent optimisations such as the use of an archive during the search and optimisation for multiple coverage criteria. Results: Our study shows that the use of a test archive makes evolutionary algorithms clearly better than random testing, and it con rms that the DynaMOSA many-objective search algorithm is the most e ective algorithm for unit test generation. Conclusions: Our results show that the choice of algorithm can have a substantial in uence on the performance of whole test suite optimisation. Although we can make a recommendation on which algorithm to use in practice, no algorithm is clearly superior in all cases, suggesting future work on improved search algorithms for unit test generatio