Jakob Pinggera

Imperative versus declarative process modeling languages : an empirical investigation

Streams of research are emerging that emphasize the advantages of using declarative process modeling languages over more traditional, imperative approaches. In particular, the declarative modeling approach is known for its ability to cope with the limited flexibility of the imperative approach. However, there is still not much empirical insight into the actual strengths and the applicability of each modeling paradigm. In this paper, we investigate in an experimental setting if either the imperative or the declarative process modeling approach is superior with respect to process model understanding. Even when task types are considered that should better match one or the other, our study finds that imperative process modeling languages appear to be connected with better understanding.

The Computer-based Health Evaluation Software (CHES): a software for electronic patient-reported outcome monitoring

BackgroundPatient-reported Outcomes (PROs) capturing e.g., quality of life, fatigue, depression, medication side-effects or disease symptoms, have become important outcome parameters in medical research and daily clinical practice. Electronic PRO data capture (ePRO) with software packages to administer questionnaires, storing data, and presenting results has facilitated PRO assessment in hospital settings. Compared to conventional paper-pencil versions of PRO instruments, ePRO is more economical with regard to staff resources and time, and allows immediate presentation of results to the medical staff.The objective of our project was to develop software (CHES – Computer-based Health Evaluation System) for ePRO in hospital settings and at home with a special focus on the presentation of individual patient’s results.MethodsFollowing the Extreme Programming development approach architecture was not fixed up-front, but was done in close, continuous collaboration with software end users (medical staff, researchers and patients) to meet their specific demands. Developed features include sophisticated, longitudinal charts linking patients’ PRO data to clinical characteristics and to PRO scores from reference populations, a web-interface for questionnaire administration, and a tool for convenient creating and editing of questionnaires.ResultsBy 2012 CHES has been implemented at various institutions in Austria, Germany, Switzerland, and the UK and about 5000 patients participated in ePRO (with around 15000 assessments in total). Data entry is done by the patients themselves via tablet PCs with a study nurse or an intern approaching patients and supervising questionnaire completion.DiscussionDuring the last decade several software packages for ePRO have emerged for different purposes. Whereas commercial products are available primarily for ePRO in clinical trials, academic projects have focused on data collection and presentation in daily clinical practice and on extending cancer registries with PRO data. CHES includes several features facilitating the use of PRO data for individualized medical decision making. With its web-interface it allows ePRO also when patients are home. Thus, it provides complete monitoring of patients‘physical and psychosocial symptom burden.

The Impact of Testcases on the Maintainability of Declarative Process Models

Declarative approaches to process modeling are regarded well suited for highly volatile environments as they provide a high degree of flexibility. However, problems in understanding and maintaining declarative process models impede their usage. To compensate for these shortcomings Test Driven Modeling has been proposed. This paper reports from a controlled experiment evaluating the impact of Test Driven Modeling, in particular the adoption of testcases, on process model maintenance. Thereby, students modified declarative process models, one model with the support of testcases and one model without the support of testcases. Data gathered in this experiment shows that the adoption of testcases significantly lowers cognitive load and increases perceived quality of changes. In addition, modelers who had testcases at hand performed significantly more change operations, while at the same time the quality of process models did not decrease.

Tracing the Process of Process Modeling with Modeling Phase Diagrams

The quality of a business process model is presumably highly dependent upon the modeling process that was followed to create it. Still, there is a lack of concepts to investigate this connection empirically. This paper introduces the formal concept of a phase diagram through which the modeling process can be analyzed, and a corresponding implementation to study a modeler’s sequence of actions. In an experiment building on these assets, we observed a group of modelers engaging in the act of modeling. The collected data is used to demonstrate our approach for analyzing the process of process modeling. Additionally, we are presenting first insights and sketch requirements for future experiments.

Tying process model quality to the modeling process: the impact of structuring, movement, and speed

In an investigation into the process of process modeling, we examined how modeling behavior relates to the quality of the process model that emerges from that. Specifically, we considered whether (i) a modelers structured modeling style, (ii) the frequency of moving existing objects over the modeling canvas, and (iii) the overall modeling speed is in any way connected to the ease with which the resulting process model can be understood. In this paper, we describe the exploratory study to build these three conjectures, clarify the experimental set-up and infrastructure that was used to collect data, and explain the used metrics for the various concepts to test the conjectures empirically. We discuss various implications for research and practice from the conjectures, all of which were confirmed by the experiment.

Toward Enhanced Life-Cycle Support for Declarative Processes

The need for flexible process‐aware information systems resulted in a recent interest in declarative approaches, as they promise a high degree of flexibility. However, the potential of current declarative approaches is impeded by deficiencies in understandability and maintainability. This paper proposes an approach toward better understandability and maintainability of declarative processes by adopting well‐established techniques from the domain of software engineering. More specifically, the ideas of test‐driven development and automated acceptance testing are adopted to interweave process specification and process testing. Thereby, during modeling, testcases balance the circumstantial/sequential information mismatch as well as improve understandability by dispensing with hard mental operations and removing hidden dependencies. Because testcases are also understandable to domain experts, they foster communication between domain experts and model builders, providing a common basis for communication. During process execution, testcases, in turn, help to document the reasons for process deviations and ensure that respective deviations can be easily considered during schema evolution. Furthermore, testcases ensure that no undesired behavior is introduced through process adaptations. Copyright

Assessing the impact of hierarchy on model understandability --- a cognitive perspective

Modularity is a widely advocated strategy for handling complexity in conceptual models. Nevertheless, a systematic literature review revealed that it is not yet entirely clear under which circumstances modularity is most beneficial. Quite the contrary, empirical findings are contradictory, some authors even show that modularity can lead to decreased model understandability. In this work, we draw on insights from cognitive psychology to develop a framework for assessing the impact of hierarchy on model understandability. In particular, we identify abstraction and the split-attention effect as two opposing forces that presumably mediate the influence of modularity. Based on our framework, we describe an approach to estimate the impact of modularization on understandability and discuss implications for experiments investigating the impact of modularization on conceptual models.

Creating Declarative Process Models Using Test Driven Modeling Suite

Declarative approaches to process modeling promise a high degree of flexibility. However, current declarative state-of-the-art modeling notations are, while sound on a technical level, hard to understand. To cater for this problem, in particular to improve the understandability of declarative process models as well as the communication between domain experts and model builders, Test Driven Modeling (TDM) has been proposed. In this tool paper we introduce Test Driven Modeling Suite (TDMS) which provides operational support for TDM. We show how TDMS realizes the concepts of TDM and how Cheetah Experimental Platform is used to make TDMS amenable for effective empirical research. Finally, we provide a brief example to illustrate how the adoption of TDMS brings out the intended positive effects of TDM for the creation of declarative process models.

Investigating expressiveness and understandability of hierarchy in declarative business process models

Hierarchy has widely been recognized as a viable approach to deal with the complexity of conceptual models. For instance, in declarative business process models, hierarchy is realized by sub-processes. While technical implementations of declarative sub-processes exist, their application, semantics, and the resulting impact on understandability are less understood yet—this research gap is addressed in this work. More specifically, we discuss the semantics and the application of hierarchy and show how sub-processes enhance the expressiveness of declarative modeling languages. Then, we turn to the influence of hierarchy on the understandability of declarative process models. In particular, we present a cognitive-psychology-based framework that allows to assess the impact of hierarchy on the understandability of a declarative process model. To empirically test the proposed framework, a combination of quantitative and qualitative research methods is followed. While statistical tests provide numerical evidence, think-aloud protocols give insights into the reasoning processes taking place when reading declarative process models.

Styles in business process modeling: an exploration and a model

Business process models are an important means to design, analyze, implement, and control business processes. As with every type of conceptual model, a business process model has to meet certain syntactic, semantic, and pragmatic quality requirements to be of value. For many years, such quality aspects were investigated by centering on the properties of the model artifact itself. Only recently, the process of model creation is considered as a factor that influences the resulting model’s quality. Our work contributes to this stream of research and presents an explorative analysis of the process of process modeling (PPM). We report on two large-scale modeling sessions involving 115 students. In these sessions, the act of model creation, i.e., the PPM, was automatically recorded. We conducted a cluster analysis on this data and identified three distinct styles of modeling. Further, we investigated how both task- and modeler-specific factors influence particular aspects of those modeling styles. Based thereupon, we propose a model that captures our insights. It lays the foundations for future research that may unveil how high-quality process models can be established through better modeling support and modeling instruction.