{"title":"GUI-Guided Repair of Mobile Test Scripts","authors":"Minxue Pan, Tongtong Xu, Yu Pei, Zhong Li, Tian Zhang, Xuandong Li","doi":"10.1109/ICSE-Companion.2019.00137","DOIUrl":null,"url":null,"abstract":"Graphical User Interface (GUI) testing has been the focus of mobile app testing. Manual test cases, containing valuable human knowledge about the apps under test, are often coded as scripts to enable automated and repeated execution for test cost reduction. Unfortunately, many test scripts may become broken due to changes made during app updates. Broken test scripts are expected to be updated for reuse; however, the maintenance cost can be high if large numbers of test scripts require manual repair. We propose an approach named METER to repairing broken test scripts automatically when mobile apps are updated. METER novelly leverages computer vision techniques to infer GUI changes between two versions from screenshots and uses the GUI changes to guide the repair of test scripts. In experiments conducted on 18 Android apps, METER was able to repair 78.3% broken test scripts.","PeriodicalId":273100,"journal":{"name":"2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSE-Companion.2019.00137","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
Graphical User Interface (GUI) testing has been the focus of mobile app testing. Manual test cases, containing valuable human knowledge about the apps under test, are often coded as scripts to enable automated and repeated execution for test cost reduction. Unfortunately, many test scripts may become broken due to changes made during app updates. Broken test scripts are expected to be updated for reuse; however, the maintenance cost can be high if large numbers of test scripts require manual repair. We propose an approach named METER to repairing broken test scripts automatically when mobile apps are updated. METER novelly leverages computer vision techniques to infer GUI changes between two versions from screenshots and uses the GUI changes to guide the repair of test scripts. In experiments conducted on 18 Android apps, METER was able to repair 78.3% broken test scripts.