Empirical Assessment of Prediction-Driven Quality Improvement

预测驱动的质量改进的实证评估

• 批准号: 0811865
• 负责人: Emmet Whitehead
• 金额: $ 32.5万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0811865&HistoricalAwards=false
• 关键词: Empirical; Assessment; Prediction; Driven; Quality

NSF Proposal 0811865Title: Empirical Assessment of Prediction-Driven Quality Improvement PI: Emmet James WhiteheadIn the last two years, software bug prediction techniques have become increasingly accurate and it is now realistic to begin using them in software engineering practice. Bug prediction technology is introduced into two industrial software projects at separate companies. The first case study will give developers bug prediction information as a form of risk awareness, then explore how the developers react to this information. The second case study will predict the most bug prone files. Quality improvement resources will then be assigned to those files to remove bugs using a combination of inspections, testing, static analysis, and re-factoring. This project will provide increased understanding of how developers alter their development behavior when faced with high quality bug prediction information. It will also increase understanding of how the application of quality improvement resources on bug-prone files affects their quality in the near and long term. It will also yield improved understanding of how mixtures of quality improving techniques (inspections, testing, static analysis, re-factoring) combine to impact software quality. The project will also create improved bug prediction techniques that connect bug predictions with operational reliability (predicting high severity bugs) and provide developer-customized bug predictions.

NSF提案0811865标题：预测驱动质量改进的经验评估PI：埃米特詹姆斯怀特黑德在过去的两年里，软件缺陷预测技术已经变得越来越准确，现在开始在软件工程实践中使用它们是现实的。错误预测技术被引入到两个不同公司的工业软件项目中。第一个案例研究将为开发人员提供错误预测信息作为风险意识的一种形式，然后探索开发人员如何对这些信息做出反应。第二个案例研究将预测最容易出现错误的文件。质量改进资源将被分配给这些文件，以使用检查、测试、静态分析和重构的组合来删除错误。这个项目将提供更多的理解，当开发人员面对高质量的错误预测信息时，他们如何改变他们的开发行为。它还将增加对质量改进资源在bug倾向文件上的应用如何影响其近期和长期质量的理解。它还将产生更好的理解质量改进技术（检查，测试，静态分析，重构）的混合物如何结合联合收割机来影响软件质量。该项目还将创建改进的错误预测技术，将错误预测与操作可靠性（预测高严重性错误）联系起来，并提供开发人员定制的错误预测。