| Reference : An Empirical Evaluation of Evolutionary Algorithms for Test Suite Generation |
| Scientific congresses, symposiums and conference proceedings : Paper published in a book | |||
| Engineering, computing & technology : Computer science | |||
| http://hdl.handle.net/10993/31408 | |||
| An Empirical Evaluation of Evolutionary Algorithms for Test Suite Generation | |
| English | |
| Campos, Jose [] | |
| Ge, Yan [] | |
| Fraser, Gordon [] | |
| Eler, Marcello [] | |
Arcuri, Andrea  [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| 2017 | |
| Symposium on Search-Based Software Engineering (SSBSE) | |
| Yes | |
| SSBSE'17 | |
| 2017 | |
| [en] Evolutionary algorithms have been shown to be effective
at generating unit test suites optimised for code coverage. While many aspects of these algorithms have been evaluated in detail (e.g., test length and different kinds of techniques aimed at improving performance, like seeding), the influence of the specific algorithms has to date seen less attention in the literature. As it is theoretically impossible to design an algorithm that is best on all possible problems, a common approach in software engineering problems is to first try a Genetic Algorithm, and only afterwards try to refine it or compare it with other algorithms to see if any of them is more suited for the addressed problem. This is particularly important in test generation, since recent work suggests that random search may in practice be equally effective, whereas the reformulation as a many-objective problem seems to be more effective. To shed light on the influence of the search algorithms, we empirically evaluate six different algorithms on a selection of non-trivial open source classes. Our study shows that the use of a test archive makes evolutionary algorithms clearly better than random testing, and it confirms that the many-objective search is the most effective. | |
| http://hdl.handle.net/10993/31408 | |
| FnR ; FNR3949772 > Lionel Briand > VVLAB > Validation and Verification Laboratory > 01/01/2012 > 31/07/2018 > 2010 |
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