Discovering Software Process Measures Using Genetic Programming

使用遗传编程发现软件过程度量

• 批准号: 9970893
• 负责人: Taghi Khoshgoftaar
• 金额: $ 20万
• 依托单位: Florida Atlantic University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-08-01 至 2003-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9970893&HistoricalAwards=false
• 关键词: Discovering; Software; Process; Measures; Using

CCR-9970893 Khoshgoftaar, Taghi M.Discovering Software Process Measures Using Genetic Programming is a research project that is developing automated methods for finding algorithms that measure novel and useful attributes of software development processes using genetic programming. A cost-effective strategy for assuring high quality software is to target enhancement activities to those modules that are most likely to have faults. When the methods being developed by this project are applied to a software development project, the resulting software process metrics will be novel, specialized for the development organization, and useful for predicting software quality. Genetic programming is a promising technology that is well-suited to searching for novel combinations of algorithmic elements that satisfy complex criteria. This project's contributions to the state of the art are empirical exploration of data stored by feature tracking systems and problem reporting systems to discover software process metrics that are useful as independent variables in software quality models, an automated method for goal-driven discovery and measurement of specialized software process metrics using genetic programming, and a demonstration of the application of genetic programming to a new domain, namely, software measurement. This improvement in the state of the art will enable software developers to achieve practical, cost-effective process improvement guided by predictions from software quality models at a new level of accuracy and robustness.

CCR-9970893 Khoshgoftaar，Taghi M.《使用遗传编程发现软件过程度量》是一个研究项目，该项目正在开发自动化方法，用于寻找使用遗传编程度量软件开发过程的新颖和有用属性的算法。 确保高质量软件的一个成本有效的策略是针对那些最有可能出现故障的模块进行增强活动。 当本项目开发的方法应用于软件开发项目时，所得到的软件过程度量将是新颖的，专门用于开发组织，并用于预测软件质量。 遗传编程是一种很有前途的技术，非常适合于搜索满足复杂标准的算法元素的新组合。 该项目对最新技术水平的贡献是对特征跟踪系统和问题报告系统存储的数据进行经验探索，以发现在软件质量模型中作为独立变量有用的软件过程度量，使用遗传编程的目标驱动发现和测量专用软件过程度量的自动化方法，以及遗传编程应用于新领域的演示，即软件度量。 这种最新技术水平的改进将使软件开发人员能够在软件质量模型预测的指导下，以新的准确性和鲁棒性水平实现实用、经济高效的流程改进。