This paper presents COnfECt, a model learning approach, which aims at recovering the functioning of a component-based system from its execution traces. We refer here to non concurrent systems whose internal interactions among components are not observable from the environment. COnfECt is specialised into the detection of components of a black-box system and in the inference of models called systems of labelled transition systems (LTS). COnfECt tries to detect components and their specific behaviours in traces, then it generates LTS for every component discovered, which captures its behaviours. Besides, it synchronises the LTSs together to express the functioning of the whole system. COnfECt relies on machine learning techniques to build models: it uses the notion of correlation among actions in traces to detect component behaviours and exploits a clustering technique to merge similar LTSs and synchronise them. We describe the three steps of COnfECt and the related algorithms in this paper. Then, we present some preliminary experimentations.

中文翻译：

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基于组件的系统的模型生成

本文介绍了 COnfECt，一种模型学习方法，旨在从其执行跟踪中恢复基于组件的系统的功能。我们在这里指的是非并发系统，其组件之间的内部交互无法从环境中观察到。COnfECt 专门用于检测黑盒系统的组件，以及称为标记转换系统 (LTS) 系统的模型的推断。COnfECt 尝试在跟踪中检测组件及其特定行为，然后为发现的每个组件生成 LTS，从而捕获其行为。此外，它将 LTS 同步在一起以表达整个系统的功能。COnfECt 依赖机器学习技术来构建模型：它使用跟踪中动作之间相关性的概念来检测组件行为，并利用聚类技术来合并相似的 LTS 并同步它们。我们在本文中描述了 COnfECt 的三个步骤和相关算法。然后，我们提出了一些初步的实验。