F. Khalili, Ali Mohebbi, Valerio Terragni, M. Pezzè, L. Mariani, A. Heydarnoori
{"title":"特定领域词嵌入模型在GUI测试重用中的有效性","authors":"F. Khalili, Ali Mohebbi, Valerio Terragni, M. Pezzè, L. Mariani, A. Heydarnoori","doi":"10.1145/3524610.3527873","DOIUrl":null,"url":null,"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.","PeriodicalId":426634,"journal":{"name":"2022 IEEE/ACM 30th International Conference on Program Comprehension (ICPC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Ineffectiveness of Domain-Specific Word Embedding Models for GUI Test Reuse\",\"authors\":\"F. Khalili, Ali Mohebbi, Valerio Terragni, M. Pezzè, L. Mariani, A. Heydarnoori\",\"doi\":\"10.1145/3524610.3527873\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":426634,\"journal\":{\"name\":\"2022 IEEE/ACM 30th International Conference on Program Comprehension (ICPC)\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE/ACM 30th International Conference on Program Comprehension (ICPC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3524610.3527873\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE/ACM 30th International Conference on Program Comprehension (ICPC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3524610.3527873","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Ineffectiveness of Domain-Specific Word Embedding Models for GUI Test Reuse
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.