Learning-based Widget Matching for Migrating GUI Test Cases

GUI test case migration is to migrate GUI test cases from a source app to a target app. The key of test case migration is widget matching. Recently, researchers have proposed various approaches by formulating widget matching as a matching task. However, since these matching approaches depend on stat...

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Veröffentlicht in:Proceedings / International Conference on Software Engineering S. 828 - 840
Hauptverfasser: Zhang, Yakun, Zhang, Wenjie, Ran, Dezhi, Zhu, Qihao, Dou, Chengfeng, Hao, Dan, Xie, Tao, Zhang, Lu
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: ACM 14.04.2024
Schlagworte:
Context modeling
Data models
Deep learning
Graphical user interfaces
GUI testing
Software
Software and its engineering → Software testing and debugging
Software testing
Task analysis
Test migration
Training
ISSN:1558-1225
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Beschreibung
Zusammenfassung:GUI test case migration is to migrate GUI test cases from a source app to a target app. The key of test case migration is widget matching. Recently, researchers have proposed various approaches by formulating widget matching as a matching task. However, since these matching approaches depend on static word embeddings without using contextual information to represent widgets and manually formulated matching functions, there are main limitations of these matching approaches when handling complex matching relations in apps. To address the limitations, we propose the first learning-based widget matching approach named TEMdroid ( TEst Migration) for test case migration. Unlike the existing approaches, TEMdroid uses BERT to capture contextual information and learns a matching model to match widgets. Additionally, to balance the significant imbalance between positive and negative samples in apps, we design a two-stage training strategy where we first train a hard-negative sample miner to mine hard-negative samples, and further train a matching model using positive samples and mined hard-negative samples. Our evaluation on 34 apps shows that TEM-droid is effective in event matching (i.e., widget matching and target event synthesis) and test case migration. For event matching, TEM-droid's Top1 accuracy is 76%, improving over 17% compared to baselines. For test case migration, TEMdroid's F1 score is 89%, also 7% improvement compared to the baseline approach.
ISSN:1558-1225
DOI:10.1145/3597503.3623322