Analysing the cognitive effectiveness of the WebML visual notation

• Published in: Software and systems modeling Vol. 16; no. 1; pp. 195 - 227
• Main Authors: Granada, David, Vara, Juan Manuel, Brambilla, Marco, Bollati, Verónica, Marcos, Esperanza
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-02-2017; Springer Nature B.V
• Subjects: Applications programs; Assessments; Compilers; Computer Science; Design analysis; Design engineering; Design improvements; Empirical analysis; Information Systems Applications (incl.Internet); Interpreters; IT in Business; Modelling; Programming Languages; Programming Techniques; Regular Paper; Software Engineering; Software Engineering/Programming and Operating Systems; Tasks; Cognitive effectiveness; Visual communication; Web modelling language (WebML); Concrete syntax; Visual notation; Visual syntax
• ISSN: 1619-1366; 1619-1374
• DOI: 10.1007/s10270-014-0447-8

WebML is a domain-specific language used to design complex data-intensive Web applications at a conceptual level. As WebML was devised to support design tasks, the need to define a visual notation for the language was identified from the very beginning. Each WebML element is consequently associated with a separate graphical symbol which was mainly defined with the idea of providing simple and expressive modelling artefacts rather than by adopting a rigorous scientific approach. As a result, the graphical models defined with WebML may sometimes prevent proper communication from taking place between the various stakeholders. In fact, this is a common issue for most of the existing model-based proposals that have emerged during the last few years under the umbrella of model-driven engineering. In order to illustrate this issue and foster in using a scientific basis to design, evaluate, improve and compare visual notations, this paper analyses WebML according to a set of solid principles, based on the theoretical and empirical evidence concerning the cognitive effectiveness of visual notations. As a result, we have identified a set of possible improvements, some of which have been verified by an empirical study. Furthermore, a number of findings, experiences and lessons learnt on the assessment of visual notations are presented.