{"title":"Risk Group Prediction of Software Projects Using Machine Learning Algorithm","authors":"Asım Kerem Hancı","doi":"10.1109/UBMK52708.2021.9558957","DOIUrl":null,"url":null,"abstract":"In our study, we predicted software projects’ risk group by using machine learning algorithms. We conducted ID3 and Naïve Bayes algorithms using ‘development source as count’, ‘software development lifecycle model’ and ‘project size’ parameters. We obtained different accuracy ratios by implementing holdout model.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 6th International Conference on Computer Science and Engineering (UBMK)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UBMK52708.2021.9558957","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In our study, we predicted software projects’ risk group by using machine learning algorithms. We conducted ID3 and Naïve Bayes algorithms using ‘development source as count’, ‘software development lifecycle model’ and ‘project size’ parameters. We obtained different accuracy ratios by implementing holdout model.