Metamodel-based model conformance and multiview consistency checking

Model-driven development, using languages such as UML and BON, often makes use of multiple diagrams (e.g., class and sequence diagrams) when modeling systems. These diagrams, presenting different views of a system of interest, may be inconsistent. A metamodel provides a unifying framework in which to ensure and check consistency, while at the same time providing the means to distinguish between valid and invalid models, that is, conformance. Two formal specifications of the metamodel for an object-oriented modeling language are presented, and it is shown how to use these specifications for model conformance and multiview consistency checking. Comparisons are made in terms of completeness and the level of automation each provide for checking multiview consistency and model conformance. The lessons learned from applying formal techniques to the problems of metamodeling, model conformance, and multiview consistency checking are summarized

Integrating knowledge accross disciplines. Experiences from the NeWater project

The starting question for this deliverable was how to create a new adaptive management concept that can integrate insights from various disciplines and connect people from different institutional backgrounds. From literature research and empirical research on the NeWater project we identified challenges for cross-disciplinary knowledge integration, we evaluated interventions for connecting multiple knowledge frames, we analyzed the process of group model building with UML and formulated recommendations. Cross-disciplinary research has arisen from a growing number of complex problems for which knowledge of a single scientific discipline or societal field is insufficient, but presents important challenges: (1) collaboration and integration of knowledge requires in depth discussions that are timeconsuming; (2) the recursive process of problem structuring and restructuring is often at odds with the sequential planning of project activities; (3) participation and mutual learning are crucial but need to be carefully structured and sequenced; and (4) management and leadership faces the difficult challenge of balancing in depth exploration with timely delivery of tangible results. We conclude with the following general recommendations for large cross-disciplinary projects: (1) including a preparatory proposal phase for thorough exploration of opportunities of between researchers and stakeholders (2) flexible funding, planning and operational arrangements to allow for a recursive research process; (3) a project size that allows frequent interaction opportunities between researchers and between researchers and stakeholders to allow for mutual learning and in depth exploration; and (4) enhancing learning opportunities from one project to the next

Literate modelling: capturing business knowledge with the UML

At British Airways, we have found during several large OO projects documented using the UML that non-technical end-users, managers and business domain experts find it difficult to understand UML visual models. This leads to problems in requirement capture and review. To solve this problem, we have developed the technique of Literate Modelling. Literate Models are UML diagrams that are embedded in texts explaining the models. In that way end-users, managers and domain experts gain useful understanding of the models, whilst object-oriented analysts see exactly and precisely how the models define business requirements and imperatives. We discuss some early experiences with Literate Modelling at British Airways where it was used extensively in their Enterprise Object Modelling initiative.We explain why Literate Modelling is viewed as one of the critical success factors for this significant project. Finally, we propose that Literate Modelling may be a valuable extension to many other object-oriented and non object-oriented visual modelling languages

Reflective visualization and verbalization of unconscious preference

A new method is presented, that can help a person become aware of his or her unconscious preferences, and convey them to others in the form of verbal explanation. The method combines the concepts of reflection, visualization, and verbalization. The method was tested in an experiment where the unconscious preferences of the subjects for various artworks were investigated. In the experiment, two lessons were learned. The first is that it helps the subjects become aware of their unconscious preferences to verbalize weak preferences as compared with strong preferences through discussion over preference diagrams. The second is that it is effective to introduce an adjustable factor into visualization to adapt to the differences in the subjects and to foster their mutual understanding.Comment: This will be submitted to KES Journa

A comparative analysis of business process modelling techniques

Business process modelling is an increasingly popular research area for both organisations and academia due to its usefulness in facilitating human understanding and communication. Several modelling techniques have been proposed and used to capture the characteristics of business processes. However, available techniques view business processes from different perspectives and have different features and capabilities. Furthermore, to date limited guidelines exist for selecting appropriate modelling techniques based on the characteristics of the problem and its requirements. This paper presents a comparative analysis of some popular business process modelling techniques. The comparative framework is based on five criteria: flexibility, ease of use, understandability, simulation support and scope. The study highlights some of the major paradigmatic differences between the techniques. The proposed framework can serve as the basis for evaluating further modelling techniques and generating selection procedures

Evaluating Visual Realism in Drawing Areas of Interest on UML Diagrams

Areas of interest (AOIs) are defined as an addition to UML diagrams: groups of elements of system architecture diagrams that share some common property. Some methods have been proposed to automatically draw AOIs on UML diagrams. However, it is not clear how users perceive the results of such methods as compared to human-drawn areas of interest. We present here a process of studying and improving the perceived quality of computer-drawn AOIs. We qualitatively evaluated how users perceive the quality of computer- and human-drawn AOIs, and used these results to improve an existing algorithm for drawing AOIs. Finally, we designed a quantitative comparison for AOI drawings and used it to show that our improved renderings are closer to human drawings than the original rendering algorithm results. The combined user evaluation, algorithmic improvements, and quantitative comparison support our claim of improving the perceived quality of AOIs rendered on UML diagrams.

Automated Mapping of UML Activity Diagrams to Formal Specifications for Supporting Containment Checking

Business analysts and domain experts are often sketching the behaviors of a software system using high-level models that are technology- and platform-independent. The developers will refine and enrich these high-level models with technical details. As a consequence, the refined models can deviate from the original models over time, especially when the two kinds of models evolve independently. In this context, we focus on behavior models; that is, we aim to ensure that the refined, low-level behavior models conform to the corresponding high-level behavior models. Based on existing formal verification techniques, we propose containment checking as a means to assess whether the system's behaviors described by the low-level models satisfy what has been specified in the high-level counterparts. One of the major obstacles is how to lessen the burden of creating formal specifications of the behavior models as well as consistency constraints, which is a tedious and error-prone task when done manually. Our approach presented in this paper aims at alleviating the aforementioned challenges by considering the behavior models as verification inputs and devising automated mappings of behavior models onto formal properties and descriptions that can be directly used by model checkers. We discuss various challenges in our approach and show the applicability of our approach in illustrative scenarios.Comment: In Proceedings FESCA 2014, arXiv:1404.043