Carlos Castro-Herrera, C. Duan, J. Cleland-Huang, B. Mobasher
{"title":"Using Data Mining and Recommender Systems to Facilitate Large-Scale, Open, and Inclusive Requirements Elicitation Processes","authors":"Carlos Castro-Herrera, C. Duan, J. Cleland-Huang, B. Mobasher","doi":"10.1109/RE.2008.47","DOIUrl":null,"url":null,"abstract":"Requirements related problems, especially those originating from inadequacies in the human-intensive task of eliciting stakeholderspsila needs and desires, have contributed to many failed and challenged software projects. This is especially true for large and complex projects in which requirements knowledge is distributed across thousands of stakeholders. This short paper introduces a new process and related framework that utilizes data mining and recommender technologies to create an open, scalable, and inclusive requirements elicitation process capable of supporting projects with thousands of stakeholders. The approach is illustrated and evaluated using feature requests mined from an open source software product.","PeriodicalId":340621,"journal":{"name":"2008 16th IEEE International Requirements Engineering Conference","volume":"136 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"57","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 16th IEEE International Requirements Engineering Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RE.2008.47","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 57
Abstract
Requirements related problems, especially those originating from inadequacies in the human-intensive task of eliciting stakeholderspsila needs and desires, have contributed to many failed and challenged software projects. This is especially true for large and complex projects in which requirements knowledge is distributed across thousands of stakeholders. This short paper introduces a new process and related framework that utilizes data mining and recommender technologies to create an open, scalable, and inclusive requirements elicitation process capable of supporting projects with thousands of stakeholders. The approach is illustrated and evaluated using feature requests mined from an open source software product.