SHF: Medium: Early Reliability Modeling and Prediction of Embedded Software Systems

为了构建高质量的软件，工程师在设计过程中尽早定量地推理关键系统属性（如性能和可靠性）变得越来越重要。理想情况下，在花费大量时间和成本进行实施之前，对这些属性进行评估。然而，在早期的设计阶段作出有用的预测是困难的，在最好的，由于许多相关因素之间的相互作用，如软件组件的复杂属性，固件的影响，以及所需的系统属性之间的冲突。在这个项目中，我们专注于设计时评估架构的一个关键属性：可靠性。 我们所追求的方法将使工程师能够建立一个多方面的，分层的系统模型，并评估其可靠性在一个增量，可扩展的方式。 此外，我们特别关注嵌入式系统领域。 嵌入式系统为这项工作提供了丰富的机会目标，因为（1）它们需要软件和执行基板的密切相互作用;（2）它们通常不受遗留问题的影响，允许更容易地引入和探索新技术，例如我们提出的技术;（3）它们通常具有严格和复杂的要求，但很少从软件架构的角度来处理。 对我们研究的广泛评估集中在两个感兴趣的指标上：易处理性（旨在解决真实的复杂系统中存在的可扩展性问题）和敏感性（旨在解决在众多设计时不确定性下我们预测的置信度问题）。

To build high quality software, it is increasingly important for engineers to reason quantitatively about critical system properties, such as performance and reliability, as early as possible in the design process. Ideally, these properties are assessed before significant time and cost are expended on implementation. However, making useful predictions in early design stages is difficult at best due to the interplay among many relevant factors, such as complex properties of software components, effects of the firmware, and conflicts between desired system attributes. In this project, we focus on design-time evaluation of architectures with respect to one key attribute: reliability. The approach we pursue will enable an engineer to build a multi-faceted, hierarchical model of a system and assess its reliability in an incremental, scalable fashion. Moreover, we particularly focus on the area of embedded systems. Embedded systems present a rich target of opportunity for this work as (1) they demand close interplay of software and execution substrate; (2) they are often unencumbered with legacy concerns, allowing easier introduction and exploration of new techniques such as the one we propose; (3) they often have stringent and complex requirements, yet are seldom approached from a software architectural perspective. An extensive evaluation of our research focuses on two measures of interest: tractability (intended to address scalability issues existing in real, complex systems) and sensitivity (intended to address issues of confidence in our predictions under numerous design-time uncertainties).