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All organizations around the World strive to deliver high-quality products and services. Business processes are the key instrument to achieve this goal. Through them, business activities can be explicitly defined and performed in a coordinated manner, allowing to create value for all process stakeholders. Process models are used to capture, conceptualize and represent business processes. Typically depicted in graphical formats, process models allow to foster the understanding of business processes and support more effective communication between the different process stakeholders. However, the inherent complexity of some processes and the complicatedness of the process modeling languages used to represent them visually can interfere with this goal. Research about the understandability of process models has addressed different aspects, notably the characteristics of different process modeling languages and their ability to express comprehensible process models. In that regard, two key language paradigms were be discerned: imperative languages depicting all the possible execution paths explicitly in the model, and declarative languageabstracting individual execution paths and rather specifying the constraints guiding the overall interplay between the process activities. While imperative languages provide adequate representations for pre-specified and repetitive processes (e.g., security checks in airports), declarative languages are rather dedicated to dynamic and flexible processes meant to fit and adapt in differentcircumstances (e.g., law-based processes). Given the flexible nature of many business processes in our World, declarative languages are good candidates for capturing and represent them in a concise manner. The flexibility granted by declarative languages requires process stakeholders to adopt a constraint-based approach to describe and interpret process models. From a cognitive perspective, this approach can challenge humans’ understanding of process models. To overcome this limitation, a set of tools and approaches have been proposed in the literature. This thesis sheds light on these mechanisms to scrutinize their support and improve their effectiveness during the modeling and comprehension of declarative process models. The carried-out studies focus on the Dynamic Condition Response (DCR) language, being one of the most adopted declarative languages in industry, thatis also supported by a wide range of tools, embedded in an online modeling platform, available for both academic and commercial uses. The DCR platform comprises a graphical editor producing declarative process models, a textual annotator mapping process specifications with the model elements and a simulation tool illustrating the execution paths allowed in the model. The research conducted as part of this thesis can be organized into three key contributions. In the first contribution, the combination of the aforementioned tools is conceived into a hybrid process artifact, which is itself, is part of a larger family of hybrid business process representations (HBPRs), that have been proposed in the literature to support the modeling and comprehension of process models. The conceptual research is followed by a systematic literature review where similar HBPRs are identified, examined and compared on different levels. In the second contribution, hybrid process artifacts are investigated in terms of their support for process modeling and model comprehension tasks. As for the third contribution, the focus is shifted to the declarative process model within the hybrid process artifact. Herein, a set of modeling practices and complexity metrics are proposed to support declarative process modeling and provide quantifiable means to assess the understandability of declarative models. The outcome of this thesis contributes to the development of new approaches and tools, providing additional support for the modeling and comprehension ofdeclarative process models and thus promoting their use in practice. |
