TestMig: migrating GUI test cases from iOS to Android

Qin, Xue; Zhong, Hao; Wang, Xiaoyin

doi:10.1145/3293882.3330575

Cited by 39 publications

(5 citation statements)

References 45 publications

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“…Through the analysis of the research topic community, we can have a more specific understanding of the current related research in this field. Excluding the field theme, in this research field, there are relatively more studies on two topics, that is, test case generation [10,[53][54][55][56][57] and automated test [10,13,20,21,40,54,56,[58][59][60][61][62][63][64][65][66].…”

Section: Rq12 Popular Topics and Relationshipsmentioning

confidence: 99%

A systematic mapping study for graphical user interface testing on mobile apps

Nie

Said²,

et al. 2023

IET Software

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Mobile apps with tested Graphical User Interface (GUI) tend to have higher downloads in the apps store. In recent years, few efforts were made to analyse the research community and research status of the literature for GUI testing on mobile apps, which brings an obstacle to characterise and understand this field. In this study, the authors propose a systematic mapping study to gain insights into the field. First, the authors conduct an extensive search of relevant literature over seven popular digital libraries. From 4427 candidate studies, 114 primary studies published between January 2011 and September 2022 were selected. Next, the authors analyse these primary studies from the perspectives of bibliometric and qualitative analysis. For the bibliometric analysis, first, the authors analyse the popular research topics and their relationships. Second, the authors study the authors' community. For the qualitative analysis, the authors analyse the objectives, approaches and evaluation metrics employed in these primary studies. Their investigation reports several major findings: (1) there are relatively more studies on two topics, that is, test case generation and the automated test; (2) the most productive authors tend to collaborate and often have relatively broad research interests; (3) the functionality is the main objective of GUI testing; the model-based approach is the most widely used.

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Section: Rq12 Popular Topics and Relationshipsmentioning

confidence: 99%

A systematic mapping study for graphical user interface testing on mobile apps

Nie

Said²,

et al. 2023

IET Software

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“…Some works focus on cross platform test-cases mitigation. Qin et al [68] propose a framework, namely TestMig to mitigate the iOS test cases to Android platform. In detail, to map the UI events, it calculate the similarity between iOS UI controls and Android UI controls based on the UI control attributes (e.g., ID, label).…”

Section: Cross-platform Test-cases Migrationmentioning

confidence: 99%

CydiOS: A Model-Based Testing Framework for iOS Apps

Zhou

et al. 2023

Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis

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To make an app stand out in an increasingly competitive market, developers must ensure its quality to deliver a better user experience. UI testing is a popular technique for quality assurance, which can thoroughly test the app from the users' perspective. However, while considerable research has already studied UI testing on the Android platform, there is no research on iOS. This paper introduces CydiOS, a novel approach to performing model-based testing for iOS apps. CydiOS enhances the existing static analysis to build a more complete static model for the app under test. We propose an approach to retrieve runtime information to obtain real-time app context that can be mapped in the model. To improve the effectiveness of UI testing, we also introduce a potential-aware search algorithm to guide testing execution. We compare CydiOS with four representative algorithms(i.e., random, depth-first, stoat, and ape). We have evaluated CydiOS on 50 popular apps from App Store, and the results show that CydiOS outperforms other tools, achieving both higher code coverage and screen coverage. We will open source CydiOS at https://github.com/SoftWare2022Testing/CydiOS, and a demo video can be found at https://www.youtube.com/shorts/VeZpY92Fno4.

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“…Moreover, Moran et al [38] implement a tool on the basis of REMAUI, namely REDRAW, which can generate codes from UI images using the deep learning technology. Additionally, Qin et al [39] present a tool, namely TestMig, for migrating test scripts from iOS to Android platform using widget hierarchy information and screenshots.…”

Section: Widget Recognition Technologymentioning

confidence: 99%

Layout and Image Recognition Driving Cross-Platform Automated Mobile Testing

Fang

Yun

et al. 2021

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)

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The fragmentation problem has extended from Android to different platforms, such as iOS, mobile web, and even mini-programs within some applications (app), like WeChat1. In such a situation, recording and replaying test scripts is one of the most popular automated mobile app testing approaches. However, such approach encounters severe problems when crossing platforms. Different versions of the same app need to be developed to support different platforms relying on different platform supports. Therefore, mobile app developers need to develop and maintain test scripts for multiple platforms aimed at completely the same test requirements, greatly increasing testing costs. However, we discover that developers adopt highly similar user interface layouts for versions of the same app on different platforms. Such a phenomenon inspires us to replay test scripts from the perspective of similar UI layouts. In this paper, we propose an image-driven mobile app testing framework, utilizing Widget Feature Matching and Layout Characterization Matching to analyze app UIs. We use computer vision (CV) technologies to perform UI feature comparison and layout hierarchy extraction on mobile app screenshots to obtainUI structures containing rich contextual information of app widgets, including coordinates, relative relationship, etc. Based on acquired UI structures, we can form a platform-independent test script, and then locate the target widgets under test. Thus, the proposed framework non-intrusively replays test scripts according to a novel platform-independent test script model. We also design and implement a tool named LIRAT to devote the proposed framework into practice, based on which, we conduct an empirical study to evaluate the effectiveness and usability of the proposed testing framework. The results show that the overall replay accuracy reaches around 65.85% on Android (8.74% improvement over state-of-the-art approaches) and 35.26% on iOS (35% improvement over state-of-the-art approaches).

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TestMig: migrating GUI test cases from iOS to Android

Cited by 39 publications

References 45 publications

A systematic mapping study for graphical user interface testing on mobile apps

A systematic mapping study for graphical user interface testing on mobile apps

CydiOS: A Model-Based Testing Framework for iOS Apps

Layout and Image Recognition Driving Cross-Platform Automated Mobile Testing

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