An increasingly used method for the engineering of software systems with strict quality-of-service (QoS) requirements involves the synthesis and verification of probabilistic models for many alternative architectures and instantiations of system parameters. Using manual trial-and-error or simple heuristics for this task often produces suboptimal models, while the exhaustive synthesis of all possible models is typically intractable. The EvoChecker search-based software engineering approach presented in our paper addresses these limitations by employing evolutionary algorithms to automate the model synthesis process and to significantly improve its outcome. EvoChecker can be used to synthesise the Pareto-optimal set of probabilistic models associated with the QoS requirements of a system under design, and to support the selection of a suitable system architecture and configuration. EvoChecker can also be used at runtime, to drive the efficient reconfiguration of a self-adaptive software system. We evaluate EvoChecker on several variants of three systems from different application domains, and show its effectiveness and applicability.

中文翻译：

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服务质量软件工程概率模型的综合

一种越来越多地用于具有严格服务质量 (QoS) 要求的软件系统工程的方法涉及对许多替代架构和系统参数实例化的概率模型的综合和验证。为此任务使用手动试错法或简单的启发式方法通常会产生次优模型，而所有可能模型的详尽综合通常是难以处理的。我们论文中提出的基于 EvoChecker 搜索的软件工程方法通过采用进化算法来自动化模型合成过程并显着改善其结果，从而解决了这些限制。EvoChecker 可用于综合与正在设计的系统的 QoS 要求相关的概率模型的帕累托最优集，并支持选择合适的系统架构和配置。EvoChecker 也可以在运行时使用，以驱动自适应软件系统的高效重新配置。我们在来自不同应用领域的三个系统的几个变体上评估了 EvoChecker，并展示了它的有效性和适用性。