Xiaosong Li, Xinran Peng, Chao Ma, Tian Liu, Zenghua Li
{"title":"基于支持向量机的软件开发项目实施效果评价研究","authors":"Xiaosong Li, Xinran Peng, Chao Ma, Tian Liu, Zenghua Li","doi":"10.1109/IAI53119.2021.9619275","DOIUrl":null,"url":null,"abstract":"The software development project implementation effect evaluation is important for assessing the software development. From the aspects of progress, cost and performance, the software development project implementation effect evaluation indicator was constructed, and the software development project implementation effect evaluation process based on support vector machine was proposed, and three support vector machine classifiers (excellent/qualified, excellent/unqualified, qualified/unqualified) were used to train and test the model with sample sets. The accuracy of the tested model was 92%, and a case analysis was carried out. The research conclusions provide effective methods for carrying out the implementation effect evaluation of software development projects.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on Software Development Project Implementation Effect Evaluation Based on Support Vector Machine\",\"authors\":\"Xiaosong Li, Xinran Peng, Chao Ma, Tian Liu, Zenghua Li\",\"doi\":\"10.1109/IAI53119.2021.9619275\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The software development project implementation effect evaluation is important for assessing the software development. From the aspects of progress, cost and performance, the software development project implementation effect evaluation indicator was constructed, and the software development project implementation effect evaluation process based on support vector machine was proposed, and three support vector machine classifiers (excellent/qualified, excellent/unqualified, qualified/unqualified) were used to train and test the model with sample sets. The accuracy of the tested model was 92%, and a case analysis was carried out. The research conclusions provide effective methods for carrying out the implementation effect evaluation of software development projects.\",\"PeriodicalId\":106675,\"journal\":{\"name\":\"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IAI53119.2021.9619275\",\"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 3rd International Conference on Industrial Artificial Intelligence (IAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IAI53119.2021.9619275","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on Software Development Project Implementation Effect Evaluation Based on Support Vector Machine
The software development project implementation effect evaluation is important for assessing the software development. From the aspects of progress, cost and performance, the software development project implementation effect evaluation indicator was constructed, and the software development project implementation effect evaluation process based on support vector machine was proposed, and three support vector machine classifiers (excellent/qualified, excellent/unqualified, qualified/unqualified) were used to train and test the model with sample sets. The accuracy of the tested model was 92%, and a case analysis was carried out. The research conclusions provide effective methods for carrying out the implementation effect evaluation of software development projects.