Models@run.time
Models@run.time
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DOI：
10.1007/978-3-319-08915-7
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发表时间：
2014-12
期刊：
影响因子：
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通讯作者：
N. Bencomo;B. Cheng;U. Assmann
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文献类型：
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作者：
N. Bencomo;B. Cheng;U. Assmann
传统上，对模型驱动工程（MDE）的研究主要集中在设计，实现和验证阶段的开发模型的使用。这项工作产生了相对成熟的技术和工具，目前正在工业界和学术界使用。然而，软件模型也有可能在运行时使用，以监视和验证运行时行为的特定方面，并实现自我 * 能力（例如，自修复，自管理，自优化系统中使用的自适应技术）。在运行时使用模型的一个关键好处是，它们可以为与自主和自适应系统相关的运行时系统关注点相关的运行时决策提供更丰富的语义基础。本书是Dagstuhl研讨会11481的成果之一。时间举行的2011年11月/12月，讨论的基础，技术，机制，最先进的，研究的挑战，和应用程序的运行时模型的使用。这本书包括四个研究路线图，由Dagstuhl研讨会的原始参与者在研讨会结束后的两年中撰写，以及该领域专家的七篇研究论文。路线图文件提供了见解的关键功能，使用的运行时模型，并确定以下研究挑战：需要一个参考架构，不确定性处理的运行时模型，利用运行时模型的自适应软件的机制，并在运行时使用模型，以解决自适应系统的保证。
Traditionally, research on model-driven engineering (MDE) has mainly focused on the use of models at the design, implementation, and verification stages of development. This work has produced relatively mature techniques and tools that are currently being used in industry and academia. However, software models also have the potential to be used at runtime, to monitor and verify particular aspects of runtime behavior, and to implement self-* capabilities (eg, adaptation technologies used in self-healing, self-managing, self-optimizing systems). A key benefit of using models at runtime is that they can provide a richer semantic base for runtime decision-making related to runtime system concerns associated with autonomic and adaptive systems. This book is one of the outcomes of the Dagstuhl Seminar 11481 on models@ run. time held in November/December 2011, discussing foundations, techniques, mechanisms, state of the art, research challenges, and applications for the use of runtime models. The book comprises four research roadmaps, written by the original participants of the Dagstuhl Seminar over the course of two years following the seminar, and seven research papers from experts in the area. The roadmap papers provide insights to key features of the use of runtime models and identify the following research challenges: the need for a reference architecture, uncertainty tackled by runtime models, mechanisms for leveraging runtime models for self-adaptive software, and the use of models at runtime to address assurance for self-adaptive systems.