Supporting Development of Highly Dependable Software Through Continuous, Automated, In-process, and Individualized Software Measurement Validation

通过持续、自动化、过程中和个性化的软件测量验证支持高度可靠的软件开发

• 批准号: 0234568
• 负责人: Philip Johnson
• 金额: $ 39.87万
• 依托单位: University of Hawaii
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-01 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0234568&HistoricalAwards=false
• 关键词: Supporting; Development; Highly; Dependable; Software

Highly dependable software is, by nature, predictable. For example, one can predict with confidence the circumstances under which the software will work and under which it will fail. Empirically-based approaches to creating predictable software are based on two assumptions: (1) historical data can be used to develop and calibrate models that generate empirical predictions, and (2) there exists relationships between internal attributes of the software (i.e. concrete attributes such as size, effort, and defects) and external attributes of the software (i.e. abstract attributes such as quality or time to failure.). Software measurement validation is the process of determining a predictive relationship between available internal attributes and correspondingly useful external attributes.The general objective of this research is to design, implement, and validate software measures that support: (1) identification of fault-prone modules, enabling more efficient and effective allocation of quality assurance resources, and (2) an incremental software development method through incremental and developer-specific notifications and analyses. Empirical assessment of these methods and measures during use on the Mission data System project at Jet Propulsion Laboratory will advance both the theory and practice of software measurement validation. The research will also produce an open source software system for software measurement validation.

从本质上讲，高度可靠的软件是可预测的。例如，人们可以自信地预测软件在什么情况下可以工作，在什么情况下会失败。创建可预测软件的基于经验的方法基于两个假设：(1)历史数据可用于开发和校准生成经验预测的模型，以及(2)软件的内部属性(即，诸如大小、工作量和缺陷的具体属性)和软件的外部属性(即，诸如质量或故障时间的抽象属性)之间存在关系。软件度量验证是确定可用的内部属性和相应有用的外部属性之间的预测关系的过程。本研究的总体目标是设计、实施和验证软件度量，以支持：(1)识别容易出错的模块，使质量保证资源能够更高效和有效地分配；(2)通过增量和开发人员特定的通知和分析，实现增量软件开发方法。在喷气推进实验室的飞行任务数据系统项目中对这些方法和措施进行经验评估，将推动软件测量验证的理论和实践。这项研究还将产生一个用于软件度量验证的开源软件系统。