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An Empirical Evaluation of Search Algorithms for App Testing

Abstract : Automated testing techniques can effectively explore mobile applications in order to find faults that manifest as program crashes. A number of different techniques for automatically testing apps have been proposed and empirically compared, but previous studies focused on comparing different tools, rather than techniques. Although these studies have shown search-based approaches to be effective, it remains unclear whether superior performance of one tool compared to another is due to fundamental advantages of the underlying search technique, or due to certain engineering choices made during the implementation of the tools. In order to provide a better understanding of app testing as a search problem, we empirically study different search algorithms within the same app testing framework. Experiments on a selection of 10 non-trivial apps reveal that the costs of fitness evaluations are inhibitive, and prevent the choice of algorithm from having a major effect.
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Submitted on : Tuesday, March 31, 2020 - 3:13:12 PM
Last modification on : Tuesday, March 31, 2020 - 4:05:29 PM

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Leon Sell, Michael Auer, Christoph Frädrich, Michael Gruber, Philemon Werli, et al.. An Empirical Evaluation of Search Algorithms for App Testing. 31th IFIP International Conference on Testing Software and Systems (ICTSS), Oct 2019, Paris, France. pp.123-139, ⟨10.1007/978-3-030-31280-0_8⟩. ⟨hal-02526338⟩

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