Modelling Languages: (mostly) Concrete (Visual) Syntax. Hans Vangheluwe
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1 Modelling Languages: (mostly) Concrete (Visual) Syntax Hans Vangheluwe Antwerp 26 August 2014
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7 Causal Block Diagrams (syntax) 7
8 Causal Block Diagrams (semantics) 8
9 Operational Semantics 9
10 Causal Block Diagrams (semantics) 10
11 Formalism Transformation Graph (FTG) 11
12 Modelling Languages/Formalisms Syntax and Semantics 12
13 Modelling Languages/Formalisms Syntax and Semantics Explicit Modelling of Modelling Languages/Formalisms 13
14 Semantics of Meta-models What is the semantic domain of the Class Diagram formalism (when used as a meta-modelling language)? 14
15 Textual Languages Modelling Languages/Formalisms 15
16 Textual Languages Textual Languages this sentence is very short Individual letters in an alphabet Combined into words Combined in to sentences in a language Letters in words specified by regular expressions Words in a language specified by a grammar Symbols are combined by is to the right of 16
17 Textual Languages 17
18 Visual Languages 18
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20 Visual Languages Plex 20
21 Visual Languages Graph 21
22 Visual Languages Connection Types 22
23 Visual Languages Iconic 23
24 Visual Languages Box 24
25 Visual Languages Visual Language Classes 25
26 Visual Languages Hybrid Languages 26
27 Visual Languages Syntax-directed Visual Editors: model behaviour 27
28 Visual Languages Syntax-directed Visual Editors: model behaviour 28
29 Visual Languages Syntax-directed Visual Editors: model behaviour 29
30 Visual Languages Syntax-directed Visual Editors: freehand (early stages of multi-domain project) 30
31 Visual Languages Different Media: Gestural Interaction, Sound,... 31
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33 ``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 33 a Lascaux
34 ``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. 34
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40 ``Physics'' of Notations Communication Theory 40
41 ``Physics'' of Notations Encoding: 8 visual variables to (graphically) encode information 41
42 ``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 : brain processes textual/audio in parallel with visual data 42
43 ``Physics'' of Notations Principles for Designing Efficient and Effective Visual Notations 43
44 ``Physics'' of Notations Semiotic Clarity (semiotics = study of signs and sign processes) 1 to 1 44
45 ``Physics'' of Notations Perceptual Discriminability 45
46 ``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 size of the differences shape is the main visual variable 13 46
47 ``Physics'' of Notations Perceptual Discriminability Software Enginering notations mostly use rectangle variants Use redundant visual encoding to increase distance (e.g., textual + visual) 47
48 ``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 are intuitive 48
49 ``Physics'' of Notations Semantic Transparency 49
50 ``Physics'' of Notations Complexity management (# elements in diagram» cognitive overload) 50
51 ``Physics'' of Notations Modularization/Hierarchy 51
52 ``Physics'' of Notations Cognitive Integration (different notations) Conceptual integration (coherent mental model) Enable navigation and transition between notations 52
53 ``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) 53
54 ``Physics'' of Notations Visual Expressiveness Different visual variables have different capacity to encode information 54
55 ``Physics'' of Notations Dual Encoding Combine Textual and Visual Supplement rather than duplicate (e.g., cardinality values) Reinforce meaning 55
56 ``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? 56
57 ``Physics'' of Notations Cognitive Fit Adapt choice of visual notation to Task Audience novices and experts Adaptation may be dynamic ( learn about Task/User proficiency) Representation medium 57
58 ``Physics'' of Notations Interactions among principles 58
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