Computer Engineering / Bilgisayar Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/10
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Article Citation - WoS: 17Citation - Scopus: 20The Influence of Using Collapsed Sub-Processes and Groups on the Understandability of Business Process Models(Springer, 2020) Türetken, Oktay; Dikici, Ahmet; Vanderfeesten, Irene; Rompen, Tessa; Demirörs, OnurMany factors influence the creation of business process models which are understandable for a target audience. Understandability of process models becomes more critical when size and complexity of the models increase. Using vertical modularization to decompose such models hierarchically into modules is considered to improve their understandability. To investigate this assumption, two experiments were conducted. The experiments involved 2 large-scale real-life business process models that were modeled using BPMN v2.0 (Business Process Model and Notation) in the form of collaboration diagrams. Each process was modeled in 3 modularity forms: fully-flattened, flattened where activities are clustered using BPMN groups, and modularized using separately viewed BPMN sub-processes. The objective was to investigate if and how different forms of modularity representation (used for vertical modularization) in BPMN collaboration diagrams influence the understandability of process models. In addition to the forms of modularity representation, the presentation medium (paper vs. computer) and model reader's level of business process modeling competency were investigated as factors that potentially influence model comprehension. 60 business practitioners from a large organization and 140 graduate students participated in our experiments. The results indicate that, when these three modularity representations are considered, it is best to present the model in a 'flattened' form (with or without the use of groups) and in the 'paper' format in order to optimally understand a BPMN model. The results also show that the model reader's business process modeling competency is an important factor of process model comprehension.Article Citation - WoS: 33Citation - Scopus: 46A Semi-Automated Approach for Generating Natural Language Requirements Documents Based on Business Process Models(Elsevier Ltd., 2018) Aysolmaz, Banu; Leopold, Henrik; Reijers, Hajo A.; Demirörs, OnurContext: The analysis of requirements for business-related software systems is often supported by using business process models. However, the final requirements are typically still specified in natural language. This means that the knowledge captured in process models must be consistently transferred to the specified requirements. Possible inconsistencies between process models and requirements represent a serious threat for the successful development of the software system and may require the repetition of process analysis activities. Objective: The objective of this paper is to address the problem of inconsistency between process models and natural language requirements in the context of software development. Method: We define a semi-automated approach that consists of a process model-based procedure for capturing execution-related data in requirements models and an algorithm that takes these models as input for generating natural language requirements. We evaluated our approach in the context of a multiple case study with three organizations and a total of 13 software development projects. Results: We found that our approach can successfully generate well-readable requirements, which do not only positively contribute to consistency, but also to the completeness and maintainability of requirements. The practical use of our approach to identify a suitable subcontractor on the market in 11 of the 13 projects further highlights the practical value of our approach. Conclusion: Our approach provides a structured way to obtain high-quality requirements documents from process models and to maintain textual and visual representations of requirements in a consistent way.Article Citation - WoS: 66Citation - Scopus: 85Factors Influencing the Understandability of Process Models: a Systematic Literature Review(Elsevier Ltd., 2018) Dikici, Ahmet; Türetken, Oktay; Demirörs, OnurContext Process models are key in facilitating communication in organizations and in designing process-aware information systems. Organizations are facing increasingly larger and more complex processes, which pose difficulties to the understandability of process models. The literature reports several factors that are considered to influence the understandability of process models. However, these studies typically focus on testing of a limited set of factors. A work that collects, abstracts and synthesizes an in-depth summary of the current literature will help in developing the research in this field. Objective We conducted a systematic literature review (SLR) focusing on the empirical studies in the existing literature in order to better understand the state of the research on process model understandability, and identify the gaps and opportunities for future research. Method We searched the studies between the years 1995 and 2015 in established electronic libraries. Out of 1066 publications retrieved initially, we selected 45 publications for thorough analysis. We identified, analyzed and categorized factors that are considered to influence the understandability of process models as studied in the literature using empirical methods. We also analyzed the indicators that are used to quantify process model understandability. Results Our analysis identifies several gaps in the field, as well as issues of inconsistent findings regarding the effect of some factors, unbalanced emphasis on certain indicators, and methodological concerns. Conclusions The existing research calls for comprehensive empirical studies to contribute to a better understanding of the factors of process model understandability. Our study is a comprehensive source for researchers working on the understandability of process models and related fields, and a useful guide for practitioners aiming to generate understandable process models.