Empirical Assessment of Prediction-Driven Quality Improvement

NSF提案0811865标题：预测驱动的质量改进的经验评估PI：Emmet James Whitehead在过去的两年中，软件错误预测技术变得越来越准确，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不同公司的两个工业软件项目中。第一个案例研究将向开发人员提供错误预测信息，作为一种风险意识，然后探索开发人员对此信息的反应。第二个案例研究将预测最容易出现错误的文件。然后，将质量改进资源分配给这些文件，以使用检查、测试、静态分析和重构的组合来消除错误。这个项目将增加对开发人员在面对高质量的错误预测信息时如何改变他们的开发行为的理解。它还将增加对易出现错误的文件应用质量改进资源如何在短期和长期影响其质量的理解。它还将更好地理解质量改进技术(检查、测试、静态分析、重构)的组合如何影响软件质量。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(预测严重错误)联系起来，并提供开发人员定制的错误预测。

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.