{"title":"早期工作量估算的模糊模型树","authors":"Mohammad Azzeh, A. B. Nassif","doi":"10.1109/ICMLA.2013.115","DOIUrl":null,"url":null,"abstract":"Use Case Points (UCP) is a well-known method to estimate the project size, based on Use Case diagram, at early phases of software development. Although the Use Case diagram is widely accepted as a de-facto model for analyzing object oriented software requirements over the world, UCP method did not take sufficient amount of attention because, as yet, there is no consensus on how to produce software effort from UCP. This paper aims to study the potential of using Fuzzy Model Tree to derive effort estimates based on UCP size measure using a dataset collected for that purpose. The proposed approach has been validated against Tree boost model, Multiple Linear Regression and classical effort estimation based on the UCP model. The obtained results are promising and show better performance than those obtained by classical UCP, Multiple Linear Regression and slightly better than those obtained by Tree boost model.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"Fuzzy Model Tree for Early Effort Estimation\",\"authors\":\"Mohammad Azzeh, A. B. Nassif\",\"doi\":\"10.1109/ICMLA.2013.115\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Use Case Points (UCP) is a well-known method to estimate the project size, based on Use Case diagram, at early phases of software development. Although the Use Case diagram is widely accepted as a de-facto model for analyzing object oriented software requirements over the world, UCP method did not take sufficient amount of attention because, as yet, there is no consensus on how to produce software effort from UCP. This paper aims to study the potential of using Fuzzy Model Tree to derive effort estimates based on UCP size measure using a dataset collected for that purpose. The proposed approach has been validated against Tree boost model, Multiple Linear Regression and classical effort estimation based on the UCP model. The obtained results are promising and show better performance than those obtained by classical UCP, Multiple Linear Regression and slightly better than those obtained by Tree boost model.\",\"PeriodicalId\":168867,\"journal\":{\"name\":\"2013 12th International Conference on Machine Learning and Applications\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 12th International Conference on Machine Learning and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLA.2013.115\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 12th International Conference on Machine Learning and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLA.2013.115","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Use Case Points (UCP) is a well-known method to estimate the project size, based on Use Case diagram, at early phases of software development. Although the Use Case diagram is widely accepted as a de-facto model for analyzing object oriented software requirements over the world, UCP method did not take sufficient amount of attention because, as yet, there is no consensus on how to produce software effort from UCP. This paper aims to study the potential of using Fuzzy Model Tree to derive effort estimates based on UCP size measure using a dataset collected for that purpose. The proposed approach has been validated against Tree boost model, Multiple Linear Regression and classical effort estimation based on the UCP model. The obtained results are promising and show better performance than those obtained by classical UCP, Multiple Linear Regression and slightly better than those obtained by Tree boost model.