企业应用中软件开发分析的生产力度量的自然语言处理

Steven Delaney, Christopher Chan, Doug Smith
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引用次数: 0

摘要

在本文中,我们利用基于本体的信息提取,从400个企业级软件开发项目的软件需求规范文档语料库中进行语义分析和术语链接。该本体的目的是在企业级规范文档上执行半监督学习,以实现为软件开发分析定义生产力度量的自动化方法。为了客观地度量软件开发项目的生产力,并在与类似的软件开发概要或这些概要的基准相比较时确定软件开发中的改进领域,在生产力的上下文中对企业级软件开发项目进行概要分析是必要的。我们开发了一种半新颖的方法,将NLP OBIE技术应用于确定软件开发生产力度量,并在多个实际的企业级软件项目中评估了这种方法。
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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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