Scalable design and performance analysis for long-living software families (DAPS2)

长期存在的软件系统通常以丰富的变体集提供，以处理不同的客户需求和应用程序上下文。此外，用户通常可以在线更改为不同的配置，以动态适应不同的环境条件。除了满足功能需求之外，这些更改还可以保留现有的服务水平协议。这个项目的重点是定义一种表达系统可变性及其对性能影响的方法。由于它在某些领域的广泛使用，我们考虑了一种基于行为模型的模型驱动方法，使用增量建模和特征组合的概念，这些概念丰富了自动导出性能模型所需的信息。我们关注的是现实系统模型中通常非常大量的变化，这阻碍了基于预测目的的详尽探索的朴素方法，特别是在运行时，当对执行时间的需求是严格的。我们提供了一种共生方法，利用由delta推断的变异性结构来有效地分析整个构型空间，例如通过修剪某些可证明较差的估计性能的子空间。该方法将实际应用于软件控制的自动化装配线的动态吞吐量优化，选择作为变体丰富的长寿命软件系统的代表性案例研究，其中更改理想地在线应用以避免代价高昂的中断。

Long-living software systems are typically available in a rich set of variants to deal with differing customer requirements and application contexts. Furthermore, users are often given the possibility to change to a different configuration online to dynamically adapt to varying environmental conditions. In addition to satisfying functional requirements, such changes are to preserve existing service-level agreements. The focus of this project is to define a methodology for expressing system variability and its impact on performance. Motivated by its widespread use in certain domains, we consider a model-driven approach based on behavioral models, using notions of delta modeling and feature composition enriched with information needed to automatically derive a performance model. We are concerned with the usually very large number of variations in models of realistic systems, which impede naive approaches based on exhaustive exploration for predictive purposes, especially at runtime when requirements on execution times are stringent. We offer a symbiotic approach which harnesses a structure of variability inferred by deltas to efficiently analyzing the whole configuration space, for instance by pruning certain subspaces with provably inferior estimated performance. The approach will be practically applied to the dynamic throughput optimization of a software-controlled automated assembly line, chosen as a representative case study of variant-rich long-lived software systems where changes are ideally applied online to avoid costly interruptions.