Model inference of Mobile Applications with dynamic state abstraction

Abstract : We propose an automatic testing method of mobile applications, which also learns formal models expressing navigational paths and application states. We focus on the quality of the models to later perform analysis (verification or test case generation). In this context, our algorithm infers formal and exact models that capture the events applied while testing, the content of the observed screens and the application environment changes. A key feature of the algorithm is that it avoids the state space explosion problem by dynamically constructing state equivalence classes to slice the state space domain of an application in a finite manner and to explore these equivalence classes. We implemented this algorithm on the tool MCrawlT that was used for experimentations. The results show that MCrawlT achieves significantly better code coverage than several available tools in a given time budget.
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Contributor : Sébastien Salva <>
Submitted on : Thursday, February 14, 2019 - 2:28:59 PM
Last modification on : Monday, January 20, 2020 - 12:12:06 PM
Long-term archiving on: Wednesday, May 15, 2019 - 5:55:10 PM


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  • HAL Id : hal-02019290, version 1


Sébastien Salva, Patrice Laurencot, Stassia Zafimiharisoa. Model inference of Mobile Applications with dynamic state abstraction. Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2015, 612, 2016, Studies in Computational Intelligence (SCI). ⟨hal-02019290⟩



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