{"title":"企业应用中软件开发分析的生产力度量的自然语言处理","authors":"Steven Delaney, Christopher Chan, Doug Smith","doi":"10.1145/3299819.3299830","DOIUrl":null,"url":null,"abstract":"In this paper, we utilize ontology-based information extraction for semantic analysis and terminology linking from a corpus of software requirement specification documents from 400 enterprise-level software development projects. The purpose for this ontology is to perform semi-supervised learning on enterprise-level specification documents towards an automated method of defining productivity metrics for software development profiling. Profiling an enterprise-level software development project in the context of productivity is necessary in order to objectively measure productivity of a software development project and to identify areas of improvement in software development when compared to similar software development profiles or benchmark of these profiles. We developed a semi-novel methodology of applying NLP OBIE techniques towards determining software development productivity metrics, and evaluated this methodology on multiple practical enterprise-level software projects.","PeriodicalId":119217,"journal":{"name":"Artificial Intelligence and Cloud Computing Conference","volume":"78 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Natural Language Processing for Productivity Metrics for Software Development Profiling in Enterprise Applications\",\"authors\":\"Steven Delaney, Christopher Chan, Doug Smith\",\"doi\":\"10.1145/3299819.3299830\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we utilize ontology-based information extraction for semantic analysis and terminology linking from a corpus of software requirement specification documents from 400 enterprise-level software development projects. The purpose for this ontology is to perform semi-supervised learning on enterprise-level specification documents towards an automated method of defining productivity metrics for software development profiling. Profiling an enterprise-level software development project in the context of productivity is necessary in order to objectively measure productivity of a software development project and to identify areas of improvement in software development when compared to similar software development profiles or benchmark of these profiles. We developed a semi-novel methodology of applying NLP OBIE techniques towards determining software development productivity metrics, and evaluated this methodology on multiple practical enterprise-level software projects.\",\"PeriodicalId\":119217,\"journal\":{\"name\":\"Artificial Intelligence and Cloud Computing Conference\",\"volume\":\"78 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Artificial Intelligence and Cloud Computing Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3299819.3299830\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Intelligence and Cloud Computing Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3299819.3299830","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Natural Language Processing for Productivity Metrics for Software Development Profiling in Enterprise Applications
In this paper, we utilize ontology-based information extraction for semantic analysis and terminology linking from a corpus of software requirement specification documents from 400 enterprise-level software development projects. The purpose for this ontology is to perform semi-supervised learning on enterprise-level specification documents towards an automated method of defining productivity metrics for software development profiling. Profiling an enterprise-level software development project in the context of productivity is necessary in order to objectively measure productivity of a software development project and to identify areas of improvement in software development when compared to similar software development profiles or benchmark of these profiles. We developed a semi-novel methodology of applying NLP OBIE techniques towards determining software development productivity metrics, and evaluated this methodology on multiple practical enterprise-level software projects.
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