{"title":"A Survey on the Differences of Using User Story and Tasks in the ASD Effort Estimation in Brazil","authors":"Diego de Morais, J. Almeira, F. Siqueira","doi":"10.5753/cibse.2022.20961","DOIUrl":null,"url":null,"abstract":"This paper investigates the state of the practice of ASD estimation based on User Stories. We conducted a survey with 85 Brazilian professionals experienced in ASD estimating. The survey analyzes what is used in the estimation (User Story, task, or both), its differences, how the estimate is made (especially if there is any segmentation), and the average precision of the effort estimates. The main findings are: 1) Planning Poker is the most used technique and points with a Fibonacci scale as a metric; 2) User Stories are broken down into tasks in the vast majority of teams; 3) Teams that estimate both: User Stories and tasks/subtasks showed greater accuracy compared to the others; 4)At least ¼ of the teams make estimates for the team segmenting by some criteria.","PeriodicalId":146286,"journal":{"name":"Conferencia Iberoamericana de Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Conferencia Iberoamericana de Software Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5753/cibse.2022.20961","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper investigates the state of the practice of ASD estimation based on User Stories. We conducted a survey with 85 Brazilian professionals experienced in ASD estimating. The survey analyzes what is used in the estimation (User Story, task, or both), its differences, how the estimate is made (especially if there is any segmentation), and the average precision of the effort estimates. The main findings are: 1) Planning Poker is the most used technique and points with a Fibonacci scale as a metric; 2) User Stories are broken down into tasks in the vast majority of teams; 3) Teams that estimate both: User Stories and tasks/subtasks showed greater accuracy compared to the others; 4)At least ¼ of the teams make estimates for the team segmenting by some criteria.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
