{"title":"软件度量估计:使用集成数据分析方法的实证研究","authors":"D. Deng, M. Purvis","doi":"10.1109/ICSSSM.2007.4280207","DOIUrl":null,"url":null,"abstract":"Automatic software effort estimation is important for quality management in the software development industry, but it still remains a challenging issue. In this paper we present an empirical study on the software effort estimation problem using a benchmark dataset. A number of machine learning techniques are employed to construct an integrated data analysis approach that extracts useful information from visualisation, feature selection, model selection and validation.","PeriodicalId":153603,"journal":{"name":"2007 International Conference on Service Systems and Service Management","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Software Metric Estimation: An Empirical Study Using An Integrated Data Analysis Approach\",\"authors\":\"D. Deng, M. Purvis\",\"doi\":\"10.1109/ICSSSM.2007.4280207\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Automatic software effort estimation is important for quality management in the software development industry, but it still remains a challenging issue. In this paper we present an empirical study on the software effort estimation problem using a benchmark dataset. A number of machine learning techniques are employed to construct an integrated data analysis approach that extracts useful information from visualisation, feature selection, model selection and validation.\",\"PeriodicalId\":153603,\"journal\":{\"name\":\"2007 International Conference on Service Systems and Service Management\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-06-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 International Conference on Service Systems and Service Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSSSM.2007.4280207\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 International Conference on Service Systems and Service Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSSSM.2007.4280207","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Software Metric Estimation: An Empirical Study Using An Integrated Data Analysis Approach
Automatic software effort estimation is important for quality management in the software development industry, but it still remains a challenging issue. In this paper we present an empirical study on the software effort estimation problem using a benchmark dataset. A number of machine learning techniques are employed to construct an integrated data analysis approach that extracts useful information from visualisation, feature selection, model selection and validation.