Model Inference and Automatic Testing of Mobile Applications

Abstract : We consider, in this paper, the problem of automatically testing Mobile applications while inferring formal models expressing their functional behaviours. We propose a framework called MCrawlT, which performs automatic testing through application interfaces and collects interface changes to incrementally infer models expressing the navigational paths and states of the applications under test. These models could be later used for comprehension aid or to carry out some tasks automatically, e.g., the test case generation. The main contributions of this paper can be summarised as follows: we introduce a flexible Mobile application model that allows the definition of state abstraction with regard to the application content. This definition also helps define state equivalence classes that segment the state space domain. Our approach supports different exploration strategies by applying the Ant Colony Optimisation technique. This feature offers the advantage to change the exploration strategy by another one as desired. The performances of MCrawlT in terms of code coverage, execution time, and bug detection are evaluated on 30 Android applications and compared to other tools found in the literature. The results show that MCrawlT achieves significantly better code coverage in a given time budget.
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Contributor : Sébastien Salva <>
Submitted on : Thursday, February 14, 2019 - 4:41:22 PM
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Sébastien Salva, Patrice Laurencot. Model Inference and Automatic Testing of Mobile Applications. International Journal On Advances in Software, IARIA, 2015. ⟨hal-02019691⟩

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