{"title":"基于机器学习算法的软件项目风险群预测","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":"{\"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}","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}
Risk Group Prediction of Software Projects Using Machine Learning Algorithm
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.