Context Mobile apps (software) are used in almost all aspects of daily life by billions of people. Given the widespread use of mobile apps in various domains, the demand for systematic testing of their Graphical User Interfaces (GUI) is crucial. Despite the significant advances in automated mobile app testing over the last decade, certain challenges remain, most notably the app-specific GUI test-oracle problem, which can significantly hinder the effective detection of defects in mobile apps. In this study, we introduce the use of GUI-level invariants, referred to as GUI invariants, as app-specific GUI oracles in GUI test cases to address this challenge. Methods We propose a semi-automatic solution to extract GUI invariants and use them as app-specific GUI oracles in test cases. We use the mutation testing technique to evaluate the (fault detection) effectiveness of the GUI oracles used. In addition, we evaluate their quality aspects, namely correctness, understandability, and compatibility, from the perspective of human experts using a questionnaire survey. Results The empirical results show that the GUI oracles used are effective and helpful, as they improved the fault-detection effectiveness of the empirical test suites ranging from 18% to 32%. These results also highlight the efficacy of GUI oracles used in identifying various defects, including crashing and non-crashing functional issues, and surpassing the performance of existing tools in fault-detection rates. Additionally, the questionnaire survey outcomes indicate that the GUI oracles used are correct, understandable, and compatible. Conclusions Based on the empirical results, we can conclude that using GUI invariants as GUI oracles can be useful and effective in mobile app testing.

Extraction and empirical evaluation of GUI-level invariants as GUI Oracles in mobile app testing / Yarifard, Ali Asghar; Araban, Saeed; Paydar, Samad; Garousi, Vahid; Morisio, Maurizio; Coppola, Riccardo. - In: INFORMATION AND SOFTWARE TECHNOLOGY. - ISSN 0950-5849. - 177:(2025). [10.1016/j.infsof.2024.107531]

Extraction and empirical evaluation of GUI-level invariants as GUI Oracles in mobile app testing

Morisio, Maurizio;Coppola, Riccardo
2025

Abstract

Context Mobile apps (software) are used in almost all aspects of daily life by billions of people. Given the widespread use of mobile apps in various domains, the demand for systematic testing of their Graphical User Interfaces (GUI) is crucial. Despite the significant advances in automated mobile app testing over the last decade, certain challenges remain, most notably the app-specific GUI test-oracle problem, which can significantly hinder the effective detection of defects in mobile apps. In this study, we introduce the use of GUI-level invariants, referred to as GUI invariants, as app-specific GUI oracles in GUI test cases to address this challenge. Methods We propose a semi-automatic solution to extract GUI invariants and use them as app-specific GUI oracles in test cases. We use the mutation testing technique to evaluate the (fault detection) effectiveness of the GUI oracles used. In addition, we evaluate their quality aspects, namely correctness, understandability, and compatibility, from the perspective of human experts using a questionnaire survey. Results The empirical results show that the GUI oracles used are effective and helpful, as they improved the fault-detection effectiveness of the empirical test suites ranging from 18% to 32%. These results also highlight the efficacy of GUI oracles used in identifying various defects, including crashing and non-crashing functional issues, and surpassing the performance of existing tools in fault-detection rates. Additionally, the questionnaire survey outcomes indicate that the GUI oracles used are correct, understandable, and compatible. Conclusions Based on the empirical results, we can conclude that using GUI invariants as GUI oracles can be useful and effective in mobile app testing.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2991425