Given the increasing amount of data available in organizational systems, there is an opportunity for early requirements engineering (RE) activities to be better based on evidence than ever before. Process mining (PM) has been used for over two decades to discover and analyze as-is process models from event logs extracted from such data, with outputs often in the form of Petri Nets, directly-follows graphs, or BPMN models. This paper aims to make Use Case Map (UCM) models, from ITU-T's User Requirements Notation (URN), a first-class output of process discovery, so that mined behavior can be used in URN-based modeling, analysis, and management activities. This paper contributes and illustrates PM4Py-UCM, an open-source extension to the existing PM4Py Python library. This new tool contributes 1) a UCM discovery pipeline, 2) hierarchical decomposition strategies producing nested UCM models, 3) configurable performer mappings for UCM and BPMN visualizations, and 4) an exporter to a URN tool (jUCMNav) that preserves the mined model under round-trip. Using public and synthetic event logs, the paper showcases how the same behavior is rendered under different performer abstractions and decomposition strategies, and discusses how PM can become a practical instrument for model-driven RE.

翻译：随着组织系统中可用数据量的不断增加，早期需求工程（RE）活动比以往任何时候都更有机会建立在实证证据基础上。流程挖掘（PM）在二十多年间一直被用于从此类数据提取的事件日志中发现和分析当前流程模型，其输出形式常为Petri网、直接跟随图或BPMN模型。本文旨在将国际电信联盟（ITU-T）用户需求符号（URN）框架中的用例图（UCM）模型提升为流程发现的一等输出，从而使得挖掘出的行为能够用于基于URN的建模、分析与管理活动。本文贡献并展示了PM4Py-UCM——一个对现有PM4Py Python库的开源扩展。该新工具提供了：1）UCM发现流水线；2）生成嵌套UCM模型的分层分解策略；3）UCM与BPMN可视化中可配置的执行者映射；4）支持往返过程保留挖掘模型的URN工具（jUCMNav）导出器。通过使用公开事件日志与合成事件日志，本文展示了相同行为在不同执行者抽象与分解策略下的呈现方式，并探讨了流程挖掘如何成为模型驱动需求工程的实用工具。