一句话：提出模型驱动框架，用可变数据流与可配置硬件建模，经设计空间映射和变体感知验证自动选出最优系统变体。
嵌入式系统（如汽车领域）须满足严格的功能与非功能需求，工程师面临软硬件层面的大量设计备选方案，并需在可能相互冲突的质量属性（如制造成本与执行速度）之间选出最优解。本文提出一个模型驱动框架辅助该决策：它以可变数据流和可配置硬件平台的形式捕获系统高层规约；映射算法据此导出设计空间，即应用与平台变体的兼容配对集合，并生成变体感知的可执行模型，编码所有可行系统变体的功能与非功能行为；新的验证算法则高效定位最优系统变体。作者通过汽车工业的真实案例研究评估了该方法的收益。
Embedded systems, like those found in the automotive domain, must comply with stringent functional and non-functional requirements. To fulfil these requirements, engineers are confronted with a plethora of design alternatives both at the software and hardware level, out of which they must select the optimal solution wrt. possibly-antagonistic quality attributes (e.g. cost of manufacturing vs. speed of execution). We propose a model-driven framework to assist engineers in this choice. It captures high-level specifications of the system in the form of variable dataflows and configurable hardware platforms. A mapping algorithm then derives the design space, i.e. the set of compatible pairs of application and platform variants, and a variability-aware executable model, which encodes the functional and non-functional behaviour of all viable system variants. Novel verification algorithms then pinpoint the optimal system variants efficiently. The benefits of our approach are evaluated through a real-world case study from the automotive industry.
@inproceedings{LazregCCH019,
title = {Multifaceted Automated Analyses for Variability-Intensive Embedded Systems},
author = {Sami Lazreg and Maxime Cordy and Philippe Collet and Patrick Heymans and Sebastien Mosser},
booktitle = {ICSE},
year = {2019},
doi = {10.1109/icse.2019.00092}
}