Variability models are used to build configurators, for guiding users through the configuration process to reach the desired setting that fulfils user requirements. The same variability model can be used to design different configurators employing different techniques. One of the design options that can change in a configurator is the configuration workflow, i.e., the order and sequence in which the different configuration elements are presented to the configuration stakeholders. When developing a configurator, a challenge is to decide the configuration workflow that better suits stakeholders according to previous configurations. For example, when configuring a Linux distribution the configuration process starts by choosing the network or the graphic card and then, other packages concerning a given sequence. In this paper, we present COnfiguration workfLOw proceSS mIning (COLOSSI), a framework that can automatically assist determining the configuration workflow that better fits the configuration logs generated by user activities given a set of logs of previous configurations and a variability model. COLOSSI is based on process discovery, commonly used in the process mining area, with an adaptation to configuration contexts. Derived from the possible complexity of both logs and the discovered processes, often, it is necessary to divide the traces into small ones. This provides an easier configuration workflow to be understood and followed by the user during the configuration process. In this paper, we apply and compare four different techniques for the traces clustering: greedy, backtracking, genetic and hierarchical algorithms. Our proposal is validated in three different scenarios, to show its feasibility, an ERP configuration, a Smart Farming, and a Computer Configuration. Furthermore, we open the door to new applications of process mining techniques in different areas of software product line engineering along with the necessity to apply clustering techniques for the trace preparation in the context of configuration workflows.

可变性模型用于构建配置器，以指导用户完成配置过程以达到满足用户要求的所需设置。相同的可变性模型可用于设计采用不同技术的不同配置器。可以在配置器中更改的设计选项之一是配置工作流，即将不同配置元素呈现给配置涉众的顺序和顺序。开发配置器时，面临的挑战是根据以前的配置来确定更适合涉众的配置工作流程。例如，当配置Linux发行版时，配置过程首先选择网络或图形卡，然后选择与给定序列有关的其他软件包。在本文中，我们提出了配置工作流程简化（COLOSSI），该框架可以自动帮助确定配置工作流程，使其更适合用户活动（给定一组先前配置的日志和可变性模型）生成的配置日志。COLOSSI基于过程发现，该过程发现通常在过程挖掘领域中使用，并适应于配置上下文。通常，由于日志和发现的过程可能具有复杂性，因此有必要将迹线分成小迹。这为用户在配置过程中理解和遵循提供了更简单的配置工作流程。在本文中，我们应用并比较了四种不同的迹线聚类技术：贪婪，回溯，遗传和分层算法。我们的提案在三种不同的情况下得到了验证，以显示其可行性，ERP配置，智能农场和计算机配置。此外，我们为过程挖掘技术在软件产品线工程的不同领域中的新应用打开了大门，同时也有必要在配置工作流的上下文中将聚类技术应用于跟踪准备。