Accurate Analysis of Quality Properties of Software with Observation-Based Markov Chain Refinement

Colin Paterson, R. Calinescu
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引用次数: 7

Abstract

We introduce a tool-supported method for the automated refinement of continuous-time Markov chains (CTMCs) used to assess quality properties of component-based software. Existing research focuses on improving the efficiency of CTMC analysis and on identifying new applications for this analysis. As such, ensuring that the analysis is accurate by using CTMCs that closely model the behaviour of the analysed software has received relatively little attention. Our new method addresses this gap by refining the high-level CTMC model of a component-based software system based on observations of the execution times of its components. Our refinement method reduced analysis errors by 77–90.3% for a service-based system implemented using six public web services from three different providers, improving the accuracy of the analysis and significantly reducing the risk of invalid software engineering decisions.
基于观测的马尔可夫链精化方法对软件质量特性的精确分析
我们介绍了一种工具支持的方法,用于评估基于组件的软件的质量特性的连续时间马尔可夫链(ctmc)的自动细化。现有的研究主要集中在提高CTMC分析的效率和确定该分析的新应用。因此,通过使用ctmc来确保分析的准确性,这些ctmc密切地模拟了被分析软件的行为,这一点得到的关注相对较少。我们的新方法通过细化基于组件的软件系统的高级CTMC模型来解决这个问题,该模型基于对其组件执行时间的观察。对于使用来自三个不同提供商的六个公共web服务实现的基于服务的系统,我们的改进方法将分析误差减少了77-90.3%,提高了分析的准确性,并显着降低了无效软件工程决策的风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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