Testing Updated Apps by Adapting Learned Models

• URL: http://arxiv.org/abs/2308.05549v2
• Date: Wed, 17 Apr 2024 09:21:27 GMT
• Title: Testing Updated Apps by Adapting Learned Models
• Authors: Chanh-Duc Ngo, Fabrizio Pastore, Lionel Briand,
• Abstract summary: Continuous Adaptation of Learned Models (CALM) is an automated App testing approach that efficiently test App updates. 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. Our empirical evaluation shows that CALM exercises a significantly higher proportion of updated methods and instructions than six state-of-the-art approaches.
• Score: 2.362412515574206
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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