{"title":"Predicting good requirements for in-house development projects","authors":"J. Verner, Karl Cox, S. Bleistein","doi":"10.1145/1159733.1159758","DOIUrl":null,"url":null,"abstract":"We surveyed software practitioners regarding software development practices used during recent projects. Five categories of questions broadly related to requirements were asked: the sponsor, customer/users, requirements issues, the project manager and project management, and the development process. Relationships between project factors and good requirements were investigated. We developed requirements prediction equations by dividing our response data into two data sets. Using binary logistic regression on each set we tested the equations developed. Such analysis provides us with insight into which variables are significant predictors of good requirements. The best predictors were 1) the customers/users had a high level of confidence in the development team, with 2) risks were controlled and managed by the project manager.","PeriodicalId":201305,"journal":{"name":"International Symposium on Empirical Software Engineering","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Symposium on Empirical Software Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1159733.1159758","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 24
Abstract
We surveyed software practitioners regarding software development practices used during recent projects. Five categories of questions broadly related to requirements were asked: the sponsor, customer/users, requirements issues, the project manager and project management, and the development process. Relationships between project factors and good requirements were investigated. We developed requirements prediction equations by dividing our response data into two data sets. Using binary logistic regression on each set we tested the equations developed. Such analysis provides us with insight into which variables are significant predictors of good requirements. The best predictors were 1) the customers/users had a high level of confidence in the development team, with 2) risks were controlled and managed by the project manager.