Shinpei Hayashi, H. Kazato, Takashi Kobayashi, T. Oshima, Katsuyuki Natsukawa, T. Hoshino, M. Saeki
{"title":"Guiding Identification of Missing Scenarios for Dynamic Feature Location","authors":"Shinpei Hayashi, H. Kazato, Takashi Kobayashi, T. Oshima, Katsuyuki Natsukawa, T. Hoshino, M. Saeki","doi":"10.1109/APSEC.2016.068","DOIUrl":null,"url":null,"abstract":"Feature location (FL) is an important activity for finding correspondence between software features and modules in source code. Although dynamic FL techniques are effective, the quality of their results depends on analysts to prepare sufficient scenarios for exercising the features. In this paper, we propose a technique for guiding identification of missing scenarios using the prior FL result. After applying FL, unexplored call dependencies are extracted by comparing the results of static and dynamic analyses, and analysts are advised to investigate them for finding missing scenarios. We propose several metrics that measure the potential impact of unexplored dependencies to help analysts sort out them. Through a preliminary evaluation using an example web application, we showed our technique was effective for recommending the clues to find missing scenarios.","PeriodicalId":339123,"journal":{"name":"2016 23rd Asia-Pacific Software Engineering Conference (APSEC)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 23rd Asia-Pacific Software Engineering Conference (APSEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APSEC.2016.068","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Feature location (FL) is an important activity for finding correspondence between software features and modules in source code. Although dynamic FL techniques are effective, the quality of their results depends on analysts to prepare sufficient scenarios for exercising the features. In this paper, we propose a technique for guiding identification of missing scenarios using the prior FL result. After applying FL, unexplored call dependencies are extracted by comparing the results of static and dynamic analyses, and analysts are advised to investigate them for finding missing scenarios. We propose several metrics that measure the potential impact of unexplored dependencies to help analysts sort out them. Through a preliminary evaluation using an example web application, we showed our technique was effective for recommending the clues to find missing scenarios.