CSR: SMALL: Robust Algorithms for an Open Source Software Reliability Tool

CSR：SMALL：开源软件可靠性工具的稳健算法

• 批准号: 1526128
• 负责人: Lance Fiondella
• 金额: $ 12.5万
• 依托单位: University of Massachusetts, Dartmouth
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1526128&HistoricalAwards=false
• 关键词: CSR; SMALL; Robust; Algorithms; Open

The key to the success of all software is its reliability. This project will develop an open source software reliability tool that will allow software engineers to automatically apply software reliability models to help organizations ensure that software applications they develop can operate free of failures. Traditional algorithms are numerically unstable, meaning that they can fail if initial estimates are inaccurate. Failure of an algorithm renders it impossible to apply a software reliability model to make useful predictions such as the amount of additional time a software application should be tested in order to achieve a desired level of reliability. The goal of this research is to develop numerically stable algorithms that will succeed even if the initial estimates are inaccurate. Expectation maximization (EM) and expectation conditional maximization (ECM) algorithms will be developed for failure rate and nonhomogeneous Poisson process (NHPP) software reliability models. Traditional EM algorithms impose restrictive assumptions that limit their application to only the simplest models, while the potential of the ECM algorithm has not been fully explored. Therefore, this research challenge will remove the restrictions of existing EM algorithms and design efficient ECM algorithms for software reliability models. Implementations of these numerically stable EM and ECM algorithms will be incorporated into the open source tool to ensure that software reliability models can be applied successfully. The enhanced stability of the algorithms and the open source nature of the tool may promote widespread use of quantitative software reliability models, enabling companies and organizations to improve time to market or field a software product.

所有软件成功的关键是其可靠性。该项目将开发一个开源软件可靠性工具，该工具将允许软件工程师自动应用软件可靠性模型，以帮助组织确保他们开发的软件应用程序可以免费运行失败。传统算法在数值上是不稳定的，这意味着如果初始估计不准确，它们可能会失败。算法的失败使得不可能应用软件可靠性模型来做出有用的预测，例如应测试软件应用程序的额外时间，以达到所需的可靠性水平。这项研究的目的是开发数值稳定的算法，即使初始估计不准确，这些算法也将成功。期望最大化（EM）和期望条件最大化（ECM）算法将以故障率和非均匀泊松工艺（NHPP）软件可靠性模型开发。传统的EM算法施加了限制性假设，将其应用仅限于最简单的模型，而ECM算法的潜力尚未得到充分探索。因此，这项研究挑战将消除对软件可靠性模型的现有EM算法的限制和设计有效的ECM算法。这些数值稳定的EM和ECM算法的实现将纳入开源工具中，以确保可以成功应用软件可靠性模型。算法的增强稳定性以及该工具的开源性质可能会促进广泛使用定量软件可靠性模型，从而使公司和组织能够改善推销或投放软件产品的时间。