Reusing test cases across similar applications can significantly reduce testing effort. Some recent test reuse approaches successfully exploit word embedding models to semantically match GUI events across Android apps. It is a common understanding that word embedding models trained on domain-specific corpora perform better on specialized tasks. Our recent study confirms this understanding in the context of Android test reuse. It shows that word embedding models trained with a corpus of the English descriptions of apps in the Google Play Store lead to a better semantic matching of Android GUI events. Motivated by this result, we hypothesize that we can further increase the effectiveness of semantic matching by partitioning the corpus of app descriptions into domain-specific corpora. Our experiments do not confirm our hypothesis. This paper sheds light on this unexpected negative result that contradicts the common understanding.

Khalili, F., Mohebbi, A., Terragni, V., Pezze, M., Mariani, L., Heydarnoori, A. (2022). The Ineffectiveness of Domain-Specific Word Embedding Models for GUI Test Reuse. In IEEE International Conference on Program Comprehension (pp.560-564). IEEE Computer Society [10.1145/3524610.3527873].

The Ineffectiveness of Domain-Specific Word Embedding Models for GUI Test Reuse

Pezze M.;Mariani L.;
2022

Abstract

Reusing test cases across similar applications can significantly reduce testing effort. Some recent test reuse approaches successfully exploit word embedding models to semantically match GUI events across Android apps. It is a common understanding that word embedding models trained on domain-specific corpora perform better on specialized tasks. Our recent study confirms this understanding in the context of Android test reuse. It shows that word embedding models trained with a corpus of the English descriptions of apps in the Google Play Store lead to a better semantic matching of Android GUI events. Motivated by this result, we hypothesize that we can further increase the effectiveness of semantic matching by partitioning the corpus of app descriptions into domain-specific corpora. Our experiments do not confirm our hypothesis. This paper sheds light on this unexpected negative result that contradicts the common understanding.
paper
Android; GUI test reuse; mobile testing; NLP; word embedding;
English
30th IEEE/ACM International Conference on Program Comprehension, ICPC 2022 - 16 May 2022 through 17 May 2022
2022
IEEE International Conference on Program Comprehension
9781450392983
2022
2022-March
560
564
none
Khalili, F., Mohebbi, A., Terragni, V., Pezze, M., Mariani, L., Heydarnoori, A. (2022). The Ineffectiveness of Domain-Specific Word Embedding Models for GUI Test Reuse. In IEEE International Conference on Program Comprehension (pp.560-564). IEEE Computer Society [10.1145/3524610.3527873].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/395073
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