{"title":"回答需求可追溯性问题","authors":"Arushi Gupta, Wentao Wang, Nan Niu, J. Savolainen","doi":"10.1145/3183440.3195049","DOIUrl":null,"url":null,"abstract":"To understand requirements traceability in practice, we present a preliminary study of identifying questions from requirements repositories and examining their answering status. Investigating four open-source projects results in 733 requirements questions, among which 43% were answered successfully, 35% were answered unsuccessfully, and 22% were not answered at all. We evaluate the accuracy of using a state-of-the-art natural language processing tool to identify the requirements questions and illuminate automated ways to classify their answering status.","PeriodicalId":121436,"journal":{"name":"Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Answering the requirements traceability questions\",\"authors\":\"Arushi Gupta, Wentao Wang, Nan Niu, J. Savolainen\",\"doi\":\"10.1145/3183440.3195049\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To understand requirements traceability in practice, we present a preliminary study of identifying questions from requirements repositories and examining their answering status. Investigating four open-source projects results in 733 requirements questions, among which 43% were answered successfully, 35% were answered unsuccessfully, and 22% were not answered at all. We evaluate the accuracy of using a state-of-the-art natural language processing tool to identify the requirements questions and illuminate automated ways to classify their answering status.\",\"PeriodicalId\":121436,\"journal\":{\"name\":\"Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3183440.3195049\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3183440.3195049","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
To understand requirements traceability in practice, we present a preliminary study of identifying questions from requirements repositories and examining their answering status. Investigating four open-source projects results in 733 requirements questions, among which 43% were answered successfully, 35% were answered unsuccessfully, and 22% were not answered at all. We evaluate the accuracy of using a state-of-the-art natural language processing tool to identify the requirements questions and illuminate automated ways to classify their answering status.