Created by W.Langdon from gp-bibliography.bib Revision:1.7421
we propose a novel multi-objective evolutionary hyper-heuristic approach, dubbed Sentinel, to automate the generation of optimal cost reduction strategies for every new SUT. We evaluate Sentinel by carrying out a thorough empirical study involving 40 releases of 10 open-source real-world software systems and both baseline and state-of-the-art strategies as a benchmark for a total of 4800 experiments, which results are evaluated with both quality indicators and statistical significance tests, following the most recent best practice in the literature.
The results show that strategies generated by Sentinel outperform the baseline strategies in 95percent of the cases always with large effect sizes, and they also obtain statistically significantly better results than state-of-the-art strategies in 88percent of the cases with large effect sizes for 95percent of them. Also, our study reveals that the mutation strategies generated by Sentinel for a given software version can be used without any loss in quality for subsequently developed versions in 95percent of the cases. These results show that Sentinel is able to automatically generate mutation strategies that reduce mutation testing cost without affecting its testing effectiveness (i.e. mutation score), thus taking off from the testers shoulders the burden of manually selecting and configuring strategies for each SUT.",
Department of Computer Science, University College London, 4919 London, London United Kingdom of Great Britain and Northern Ireland WC1E 6BT",
Genetic Programming entries for Giovani Guizzo Federica Sarro Jens Krinke Silvia Regina Vergilio