{"title":"Towards Team Formation in Software Development: A Case Study of Moodle","authors":"Noppadol Assavakamhaenghan, Ponlakit Suwanworaboon, Waralee Tanaphantaruk, Suppawong Tuarob, Morakot Choetkiertikul","doi":"10.1109/ecti-con49241.2020.9158078","DOIUrl":null,"url":null,"abstract":"Software development is a team-based intensive activity where various skills (e.g. technical and analysis skills) are required to deliver high quality outcomes. An effective team member assignment is thus a crucial process. In this paper, we propose to adopt the existing machine learning approach for team recommendation to recommend software team members who are suitable for a given task. The approach take both individual strength and collaborative efficiency among team members into account to give a recommendation. We evaluate the approach on the Moodle project, well-known open source software project. The evaluation results show that the adopted approach yields a better recommendation performance compared to the baseline (i.e. random assignment approach).","PeriodicalId":371552,"journal":{"name":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ecti-con49241.2020.9158078","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Software development is a team-based intensive activity where various skills (e.g. technical and analysis skills) are required to deliver high quality outcomes. An effective team member assignment is thus a crucial process. In this paper, we propose to adopt the existing machine learning approach for team recommendation to recommend software team members who are suitable for a given task. The approach take both individual strength and collaborative efficiency among team members into account to give a recommendation. We evaluate the approach on the Moodle project, well-known open source software project. The evaluation results show that the adopted approach yields a better recommendation performance compared to the baseline (i.e. random assignment approach).