Modelling Languages: (mostly) Concrete (Visual) Syntax Hans Vangheluwe
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1 Modelling Languages: (mostly) Concrete (Visual) Syntax Hans Vangheluwe
2 Modelling Languages/Formalisms Syntax and Semantics
3 Modelling Languages/Formalisms Syntax and Semantics Model of Graph 3
4 Textual Languages Textual Languages this sentence is very short Individual letters in an alphabet Combined into words Combined into sentences in a language Valid letters in words specified by regular expressions Valid words in a language specified by a grammar letters/words are combined by is to the right of
5 Textual Languages syntax-directed editor (textual concrete syntax)
6 syntax-directed editor (visual concrete syntax) Visual Languages
7 Visual Languages
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9 Visual Languages Plex
10 Visual Languages Graph
11 Visual Languages Connection Types
12 Visual Languages Iconic
13 Visual Languages Box
14 Visual Languages Visual Language Classes
15 Visual Languages Hybrid Languages
16 Visual Languages Syntax-directed Visual Editors: model behaviour
17 Visual Languages Syntax-directed Visual Editors: model behaviour
18 Visual Languages Generate Syntax-directed Visual Editors
19 Visual Languages Syntax-directed Visual Editors: freehand (early stages of multi-domain project)
20 Visual Languages Different Media: Gestural Interaction, Sound,... 20
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22 ``Physics'' of Notations Introduction Visual notations pre-date textual ones Visual notations are important for Modelling and Software Engineering Humans are excellent pattern recognizers Need cognitively efficient and effective notations. Cognitive effectiveness = speed, ease and accuracy with which a representation can be processed by the human mind a Lascaux
23 ``Physics'' of Notations Introduction/Rationale Visual notations are often introduced without underlying theory or rationale Many visual notations for same concepts. No rigorous way to compare effectiveness and hence no clear design goal.
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27 Maryam M. Maleki, Robert F. Woodbury, Rhys Goldstein, Simon Breslav, Azam Khan. Designing DEVS visual interfaces for end-user programmers. Simulation 91(8): (2015)
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29 ``Physics'' of Notations Communication Theory
30 ``Physics'' of Notations Encoding: 8 visual variables to (graphically) encode information
31 ``Physics'' of Notations Decoding automatic, fast, parallel slow, large effort, sequential Appropriate notations» offload some of the burden from cognitive to perceptual Note: dual channel theory : auditory/verbal channel and visual/pictorial channel are processed in parallel Richard E. Mayer, Roxana Moreno. Nine Ways to Reduce Cognitive Load in Multimedia Learning. Educational Psychologist, 38(1),
32 ``Physics'' of Notations Principles for Designing Efficient and Effective Visual Notations
33 ``Physics'' of Notations Semiotic Clarity (semiotics = study of signs and sign processes) 1-to-1
34 ``Physics'' of Notations Perceptual Discriminability
35 ``Physics'' of Notations Junaed Sattar, Gregory Dudek. Reducing Uncertainty in Human-Robot Interaction: A Cost Analysis Approach. ISER 2010:
36 ``Physics'' of Notations Perceptual Discriminability should be easy to distinguish visual symbols ability to distinguish is determined by visual distance larger visual distance» faster, more accurate recognition number of visual variables on which they differ and the magnitude of the differences shape is the main visual variable 13
37 ``Physics'' of Notations Perceptual Discriminability Software Engineering notations mostly use rectangle variants Use redundant visual encoding to increase distance (e.g., textual + visual)
38 ``Physics'' of Notations Semantic Transparency The meaning of a symbol can be inferred from its appearance (intuitive) Symbols can be: Semantically Immediate Semantically Opaque Semantically Perverse Software Engineering notations are usually abstract (non-intuitive) Domain-specific icons and visual arrangement should be intuitive
39 ``Physics'' of Notations Semantic Transparency
40 ``Physics'' of Notations Complexity management (# elements in diagram» cognitive overload)
41 ``Physics'' of Notations Modularization/Hierarchy
42 ``Physics'' of Notations Cognitive Integration (different notations) Conceptual integration (coherent mental model) Enable navigation and transition between notations
43 ``Physics'' of Notations Visual Expressiveness Number of visual variables used (UML, mostly shape, no colour) 8 degrees of visual freedom (0 = non-visual 8 = visually saturated)
44 ``Physics'' of Notations Visual Expressiveness Different visual variables have different capacity to encode information
45 ``Physics'' of Notations Dual Encoding Combine Textual and Visual Supplement rather than duplicate (e.g., multiplicity values) Reinforce meaning
46 ``Physics'' of Notations Graphic Economy Not too many symbols. If many, provide legend Limit on human discrimination capability (6 levels per variable) Upper limit on graphic complexity How?
47 ``Physics'' of Notations Cognitive Fit Adapt choice of visual notation to Task Audience (novices vs. experts) Adaptation may be dynamic ( learn about Task/User proficiency) Representation medium matters
48 ``Physics'' of Notations Interactions among principles
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