android testing; Model reuse; model-based testing; regression testing; update testing; Android testing; Automated testing; Functional correctness; Functional testing; Model based testing; Regression testing; Testing technique; Update testing; Visual inspection; Software
Résumé :
[en] Although App updates are frequent and software engineers would like to verify updated features only, automated testing techniques verify entire Apps and are thus wasting resources. We present Continuous Adaptation of Learned Models (CALM), an automated App testing approach that efficiently test App updates by adapting App models learned when automatically testing previous App versions. CALM focuses on functional testing. Since functional correctness can be mainly verified through the visual inspection of App screens, CALM minimizes the number of App screens to be visualized by software testers while maximizing the percentage of updated methods and instructions exercised. Our empirical evaluation shows that CALM exercises a significantly higher proportion of updated methods and instructions than six state-of-the-art approaches, for the same maximum number of App screens to be visually inspected. Further, in common update scenarios, where only a small fraction of methods are updated, CALM is even quicker to outperform all competing approaches in a more significant way.
Centre de recherche :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SVV - Software Verification and Validation
Disciplines :
Sciences informatiques
Auteur, co-auteur :
NGO, Chanh Duc ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust > SVV > Team Fabrizio PASTORE
PASTORE, Fabrizio ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SVV
Briand, Lionel ; School of EECS, University of Ottawa, Ottawa, Canada ; Lero SFI Centre for Software Research, University of Limerick, Limerick, Ireland
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Testing Updated Apps by Adapting Learned Models
Date de publication/diffusion :
29 juin 2024
Titre du périodique :
ACM Transactions on Software Engineering and Methodology
Huawei Technologies Co., Ltd, China NSERC Discovery and Canada Research Chair programs
Subventionnement (détails) :
This project has received funding from Huawei Technologies Co., Ltd, China, and by the NSERC Discovery and Canada Research Chair programs. Experiments presented in this article were carried out using the Grid\u20195000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr).This project has received funding from Huawei Technologies Co., Ltd, China, and by the NSERC Discovery and Canada Research Chair programs.
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