Analysing the Cognitive Effectiveness of the UCM Visual Notation of - PowerPoint PPT Presentation

Analysing the Cognitive Effectiveness of the UCM Visual Notation of the UCM Visual Notation Nicolas Genon, Daniel Amyot, Patrick Heymans SAM 2010, damyot@site.uottawa.ca

Why Visual Modelling? Why Visual Modelling? Diagrams play a critical role in discussing Diagrams play a critical role in discussing, designing and documenting systems The main reason for using diagrams is to facilitate diagrams is to facilitate communication • assumed to be more assumed to be more effective than text especially for end users 2

What makes a visual notation “ good ”? What makes a visual notation good ? Cognitive Effectiveness = speed , ease and accuracy [Larkin-87 ] 3

Problem Problem When creating or evolving the visual notation of a When creating or evolving the visual notation of a modelling language, cognitive effectiveness is not taken into consideration in a systematic way! • focus is often on abstract syntax and semantics • visual notation is the “poor cousin” in notation i l t ti i th “ i ” i t ti design, and is designed in ad hoc ways • concrete visual syntax is often thought of as a matter y g of mere aesthetics (I’m as guilty as many of you!) 4

Moreover... Moreover... Users often rely (incorrectly) on intuition leading to Users often rely (incorrectly) on intuition, leading to suboptimal communication and unintended interpretations 5

Agenda Agenda • The Physics of Notations theory (PoN) The Physics of Notations theory (PoN) • Use Case Map (UCM) notation • Analysing UCM with the Physics of Notations • Illustration of several guidelines, with results and possible improvements • More guidelines discussed in the SAM paper More guidelines discussed in the SAM paper • Full analysis available as a technical report • Related work • Observations about this theory • Conclusions and future work 6

Physics of Notations Theory Physics of Notations Theory Perceptual p Discriminability Cognitive Graphic p Fit Economy Cognitive Semiotic Semantic Integration Integration Cl Clarity it Transparency T Complexity Visual Management Expressiveness Expressiveness Dual Coding [Moody-TSE-09] 7

Physics of Notations Theory Physics of Notations Theory The principles synthesize knowledge and evidence coming from various disciplines: • Cartography • Information visualization g p y • Cognitive psychology • Linguistics • Diagrammatic reasoning • Perceptual psychology • Graphic design • Semiotics p g • HCI • Typography Main contribution: defragmentation and some metrics defined 8

Visual Variables Visual Variables Eight elementary visual variables that can be used to Eight elementary visual variables that can be used to graphically encode information [Bertin ‐ 83] 10

Use Case Maps Use Case Maps • Introduced by Buhr et al in the early 90’s Introduced by Buhr et al. in the early 90 s • Part of ITU ‐ T's User Requirements Notation • Rec Z 151 November 2008 Rec. Z.151, November 2008 • Goal modelling with GRL • Scenario modelling with UCM • The standard includes • Metamodel • Vi Visual notation l t ti • XML ‐ based interchange format The full analysis is available in a technical report [Genon ‐ UCM] 11

Use Case Maps Use Case Maps 12

Use Case Maps Use Case Maps 13

Perceptual Discriminability Cognitive Graphic Fit Economy y Cognitive Semiotic Semantic Integration Clarity Transparency Complexity Visual Management Expressiveness Dual Coding 14

Cognitive Fit Perceptual Discriminability Cognitive Graphic Fit Economy Cognitive Semiotic Semantic Integration Clarity Transparency Complexity Complexity Visual Visual Management Expressiveness Use different visual dialects when required. Dual Coding 3 ‐ way fit: 3 way fit: 1. Audience (customers, users, domain experts) 2. Representation medium (paper, whiteboard, computer) 3 T k h 3. Task characteristics t i ti Cognitive Fit helps determine which audiences, media and tasks notation improvements will target In our analysis of UCM, we considered In our analysis of UCM, we considered • notation experts … • … working mainly on computer tools, and sometimes on whiteboards and paper whiteboards and paper… • … for modelling and discussing advanced scenarios 15

Semiotic Clarity Perceptual Discriminability Cognitive Graphic Fit Economy Cognitive Semiotic Semantic Integration Clarity Transparency There should be a 1:1 correspondence between Complexity Visual Management Expressiveness Dual Coding semantic constructs and graphical symbols UCM: ‐ 55 semantic constructs 55 semantic constructs ‐ 28 symbols Anomaly types Description UCM % Symbol deficit Construct not represented by any symbol 23 42 % Symbol overload Symbol overload Single symbol representing multiple constructs Single symbol representing multiple constructs 3 3 7 % 7 % Symbol excess Single construct represented by multiple 2 4 % symbols Symbol Symbol Symbol not representing any construct Symbol not representing any construct 1 1 2 % 2 % redundancy 16

Semiotic Clarity Perceptual Discriminability Cognitive Graphic Fit Economy Cognitive Semiotic Semantic Integration Clarity Transparency There should be a 1:1 correspondence between Complexity Visual Management Expressiveness Dual Coding semantic constructs and graphical symbols Anomaly types Description UCM % Symbol deficit Construct not represented by any symbol 23 42 % UCM: (UCMMap) singleton OWPeriodic Unit Essentially: ClosedWorkload Unit OWPhaseType Unit ComponentBinding OWPoisson Unit • Variable definitions ComponentType OWUniform Unit Concern PassiveResource • D Demand d Pl PluginBinding i Bi di Plug ‐ in bindings EnumerationType ProcessingResource Disk Unit ExternalOperation Unit • ProcessingResource DSP Unit Performance annotations InBinding ProcessingResource Processor Unit Metadata Variable Boolean • Others (concerns, singleton, metadata) ( , g , ) OutBinding Variable Enumeration Variable Integer • Associate symbols to these concepts • Choose not to represent and make it explicit in the standard • Remove these concepts 20

Perceptual Discriminability Perceptual Discriminability Cognitive Graphic Fit Economy Cognitive Semiotic Semantic Integration Clarity Transparency Complexity Visual Symbols should be clearly distinguishable. Management Expressiveness Dual Coding Symbol discriminability in UCM y y • Shape • 50% of symbols are icons • the others use conventional shapes • Grain (border style) • Colour (black & white) • Size 23

Perceptual Discriminability Perceptual Discriminability Cognitive Graphic Fit Economy Cognitive Semiotic Semantic Integration Clarity Transparency Complexity Visual Symbols should be clearly distinguishable. Management Expressiveness Dual Coding Suggestions for improvement Suggestions for improvement 1. Use multiple visual variables (especially colour) Team Actor Protected Agent component Process Process Object Object (Static) Stub Dynamic Stub 24

Perceptual Discriminability Perceptual Discriminability Cognitive Graphic Fit Economy Cognitive Semiotic Semantic Integration Clarity Transparency Complexity Visual Symbols should be clearly distinguishable. Management Expressiveness Dual Coding Suggestions for improvement Suggestions for improvement 2. Choose shapes from different families Team Team Start Point / Stub AND Join Waiting Place Process Process Object j Dynamic Dynamic Empty Point AND Fork Stub Quadrilaterals 3D Ellipses Complex 25

Visual Expressiveness Perceptual Discriminability Cognitive Graphic Fit Economy Cognitive Semiotic Semantic Integration U Use the full range and capacities of visual variables th f ll d iti f i l i bl Clarity Transparency Complexity Visual Management Expressiveness Dual Coding L Location (x,y) ti ( ) Shape Primary Colour Colour Brightness Brightness S Secondary d notation Grain notation Orientation Size 26

Visual Expressiveness Perceptual Discriminability Cognitive Graphic Fit Economy Cognitive Semiotic Semantic Integration Use the full range and capacities of visual variables U th f ll d iti f i l i bl Clarity Transparency Complexity Visual Management Expressiveness Dual Coding Check « if form follows content » 27

Related Work Related Work Cognitive Dimensions of Notations [Green et al., 2006] • 13 dimensions for cognitive artefacts [7]. 13 dimensions for cognitive artefacts [7]. • Not for visual notations, no guidelines, vague empirical foundation, not falsifiable Semiotic Quality (SEQUAL) Framework [Krogstie et al 2006] Semiotic Quality (SEQUAL) Framework [Krogstie et al., 2006] • Comprehensive ontology of quality concepts • Wider in scope, with similar limitations as above, but provides measurable criteria and guidelines measurable criteria and guidelines Guidelines of Modeling [Schuette et al., 2006] • Language quality framework with 6 principles • M More about using languages, with rules of thumbs b t i l ith l f th b Seven Process Modelling Guidelines [Mendling et al., 2006] • At the instance (diagram) level. Complementary. Moody’s Framework used on ArchMate, UML, i* , and BPMN 28