Model Transformation Performance Engineering

软件系统的规模和复杂性不断增加。例如，汽车上的软件数量每十年增加一个数量级。应对这些挑战的一个趋势是模型驱动工程，它主张将模型和模型转换作为开发过程中的关键构件。例如，在汽车领域和信息系统的性能预测中，模型可以变得非常庞大。在这种情况下，模型转换执行时间长达数小时。因此，性能是模型转换的重要质量，例如，关于模型元素或总体执行时间的吞吐量。近年来，一种趋势是不仅使用模型来生成代码或在设计时分析代码，而且使用模型@run.time来分析和改变自适应系统的行为。由于这些模型需要通过模型转换来持续更新，例如，为了正确地反映系统或其环境，对于通常满足硬实时要求的嵌入式系统，需要在非常紧迫的期限内执行这些转换，即使对于小模型也是如此。没有明确支持转换工程师改进转换脚本，根据我们的经验，这会带来巨大的性能收益。遗憾的是，它需要有关模型转换引擎如何与转换脚本交互的专业知识，以利用这些性能收益。传统上，分析方法使检查系统能够在运行时识别性能热点。但它们只在编程语言级别上工作，因此不合理地适用。软件性能工程(SPE)中的现有方法可以预测软件的性能，从而在性能问题出现之前发现它们，但它们不适用于模型转换。目前还没有系统和整体地支持模型转换的性能工程的研究活动，即支持工程师开发达到所需性能的转换。拟议的项目旨在为模型转换的性能工程提供一种整体的方法，以确保转换链满足其性能和可扩展性要求。项目结果将使软件工程师能够系统地识别和可视化性能问题的原因，以及预测和改进模型转换的性能。结果将应用于不同的转换语言和互补的演示器：具有软实时需求的自适应云系统和具有硬实时需求的自适应四轴飞行器群。评估将得到用户研究的补充，以确保我们的结果真正支持工程师。

Software systems continually increase in size and complexity. For example, the amount of software in cars increases by one order of magnitude every decade. One trend to handle these challenges is Model-Driven Engineering which advocates using models and model transformations as key artifacts in the development process.Models can become huge, e.g., in the automotive domain and for the performance prediction of information systems. In such cases, model transformation execution times up to hours occur. Hence, performance is an important quality of model transformations, e.g., throughput in terms of model elements or overall execution time.In recent years, there has been a trend not only to use models to generate code or to analyze them at design time but instead to use models@run.time to be able to analyze and change the behavior of self-adaptive systems. As those models need to be updated continuously by model transformations, e.g., to properly reflect the system or its environment, the transformations need to be executed within very tight deadlines, for embedded systems often satisfying hard real-time requirements, even for small models.Engineers today mostly address performance not at all, in an ad-hoc fashion, or after first problems arise in production. There exists no explicit support for transformation engineers to improve the transformation scripts which in our experience leads to massive performance gains. Unfortunately, it requires expert knowledge about how the model transformation engines interact with the transformation script to leverage these performance gains.Traditionally, profiling approaches enable inspecting systems during runtime to identify performance hot spots. But they only work on a programming language level and, thus, are not reasonably applicable. Existing approaches in Software Performance Engineering (SPE) predict the performance of software and, thus, identify performance problems before they arise but they are not suitable for model transformations. There exist no research activities to systematically and holistically enable the performance engineering of model transformations, i.e., to support the engineer in developing transformations that achieve the required performance.The proposed project aims at providing a holistic approach for the performance engineering of model transformations to ensure that the transformation chains meet their performance and scalability requirements. The project results will enable software engineers to systematically identify and visualize causes for performance issues as well as predict and improve the performance of model transformation. The results will be applied to different transformation languages and complementary demonstrators: self-adaptive cloud systems with soft real-time requirements and self-adaptive quadrocopter swarms with hard-real-time requirements. The evaluation will be complemented by user studies to ensure that our results really support engineers.