Complexity metrics for measuring the understandability and maintainability of Business Process Models using Goal-Question-Metric (GQM)

Business Process Models (BPMs), often modeling language such as UML activity between the created using stakeholders in the can provide us a diagrams, Event- Driven Process Chains Markup Language (EPML) and Yet Another Workflow Language (YAWL), serve as a base for communication that adequate software development process. In order to fulfill this purpose, they should be easy to understand and easy to maintain. For this reason, it is useful to have measures information about understandability and maintainability of the BPM. Although there are hundreds of software complexity measures that have been described and published by many researchers over the last few decades, measuring the complexity of business process models is a rather new area of research with only a small number of contributions. In this paper, we provide a comprehensive report on how existing complexity metrics of software were adapted in order to analyze the current business process models complexity. We also proposed a Goal- Question-Metric (GQM) framework for measuring the understandability and maintainability of BPMs

Process Model Metrics for Quality Assessment of Computer-Interpretable Guidelines in PROform

Background: Clinical Practice Guidelines (CPGs) include recommendations to optimize patient care and thus have the potential to improve the quality and outcomes of healthcare. To achieve this, CPG recommendations are usually formalized in terms of Computer-Interpretable Guideline (CIG) languages. However, a clear understanding of CIG models may prove complicated, due to the inherent complexity of CPGs and the specificities of CIG languages. Drawing a parallel with the Business Process Management (BPM) and the Software Engineering fields, understandability and modifiability of CIG models can be regarded as primary quality attributes, in order to facilitate their validation, as well as their adaptation to accommodate evolving clinical evidence, by modelers (typically teams made up of clinical and IT experts). This constitutes a novel approach in this area of CIG development, where understandability and modifiability aspects have not been considered to date. Objective: In this paper, we define a comprehensive set of process model metrics for CIGs described in the PROforma CIG language, with the main objective of providing tools for quality assessment of CIG models in this language. Methods: To this end, we first reinterpret a set of metrics from the BPM field in terms of PROforma and then we define new metrics to capture the singularities of PROforma models. Additionally, we report on a set of experiments to assess the relationship between the structural and logical properties of CIG models, as measured by the proposed metrics, and their understandability and modifiability from the point of view of modelers, both clinicians and IT staff. For the analysis of the experiment results, we perform statistical analysis based on a generalized linear mixed model with binary logistic regression. Results: Our contribution includes the definition of a comprehensive set of metrics that allow measuring model quality aspects of PROforma CIG models, the implementation of tools and algorithms to assess the metrics for PROforma models, and the empirical validation of the proposed metrics as quality indicators. Conclusions: In light of the results, we conclude that the proposed metrics can be of great value, as they capture the PROforma-specific features in addition to those inspired by the general-purpose BPM metrics in the literature. In particular, the newly defined metrics for PROforma prevail as statistically significant when the whole CIG model is considered, which means that they better characterize its complexity. Consequently, the proposed metrics can be used as quality indicators of the understandability, and thereby maintainability, of PROforma CIGs

Investigating Differences between Graphical and Textual Declarative Process Models

Declarative approaches to business process modeling are regarded as well suited for highly volatile environments, as they enable a high degree of flexibility. However, problems in understanding declarative process models often impede their adoption. Particularly, a study revealed that aspects that are present in both imperative and declarative process modeling languages at a graphical level-while having different semantics-cause considerable troubles. In this work we investigate whether a notation that does not contain graphical lookalikes, i.e., a textual notation, can help to avoid this problem. Even though a textual representation does not suffer from lookalikes, in our empirical study it performed worse in terms of error rate, duration and mental effort, as the textual representation forces the reader to mentally merge the textual information. Likewise, subjects themselves expressed that the graphical representation is easier to understand

Machine learning stochastic design models.

Due to the fluid nature of the early stages of the design process, it is difficult to obtain deterministic product design evaluations. This is primarily due to the flexibility of the design at this stage, namely that there can be multiple interpretations of a single design concept. However, it is important for designers to understand how these design concepts are likely to fulfil the original specification, thus enabling the designer to select or bias towards solutions with favourable outcomes. One approach is to create a stochastic model of the design domain. This paper tackles the issues of using a product database to induce a Bayesian model that represents the relationships between the design parameters and characteristics. A greedy learning algorithm is presented and illustrated using a simple case study

A research review of quality assessment for software

Measures were recommended to assess the quality of software submitted to the AdaNet program. The quality factors that are important to software reuse are explored and methods of evaluating those factors are discussed. Quality factors important to software reuse are: correctness, reliability, verifiability, understandability, modifiability, and certifiability. Certifiability is included because the documentation of many factors about a software component such as its efficiency, portability, and development history, constitute a class for factors important to some users, not important at all to other, and impossible for AdaNet to distinguish between a priori. The quality factors may be assessed in different ways. There are a few quantitative measures which have been shown to indicate software quality. However, it is believed that there exists many factors that indicate quality and have not been empirically validated due to their subjective nature. These subjective factors are characterized by the way in which they support the software engineering principles of abstraction, information hiding, modularity, localization, confirmability, uniformity, and completeness