A Pattern Recognition Approach to Software Engineering Empirical Modeling

软件工程经验建模的模式识别方法

• 批准号: 9223138
• 负责人: Victor Basili
• 金额: $ 29.18万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-03-15 至 1997-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9223138&HistoricalAwards=false
• 关键词: Pattern; Recognition; Approach; Software; Engineering

The ability to plan, control and predict software processes is facilitated by the measurement of software development artifacts, even though construction of quantitative models from these artifacts is a difficult task. Explanatory variables are potentially numerous and heterogeneous; data sets are replete with outliers; the quality of the data collection may be questionable; and often only partial information is available for building the model. Even so, there is a need for customized modelling techniques, procedures, and tools to facilitate the construction of local tools within a specific development environment. Stochastic modelling is needed for three kinds of activity within the development process: planning, monitoring and control, and post-mortem analysis and evaluation. Models are used to enable managers to plan and to take corrective actions to meet budget, schedule and quality requirements. This research is aimed at continuing the development of an approach called Optimized Set Reduction with the aim of addressing the specific constraints and needs of software engineering.

软件开发工件的测量促进了计划、控制和预测软件过程的能力，尽管从这些工件构建定量模型是一项艰巨的任务。 解释变量可能数量众多且异质；数据集充满了异常值；数据收集的质量可能存在问题；通常只有部分信息可用于构建模型。 即便如此，仍然需要定制的建模技术、过程和工具，以促进在特定开发环境中构建本地工具。 开发过程中的三类活动需要随机建模：规划、监测和控制以及事后分析和评估。 模型用于使管理人员能够规划并采取纠正措施，以满足预算、进度和质量要求。 这项研究旨在继续开发一种称为“优化集缩减”的方法，旨在解决软件工程的特定约束和需求。