This document summarizes Daniel Moody's research on establishing scientific principles for constructing effective visual notations in software engineering. It discusses Moody's descriptive theory that explains how visual notations communicate through encoding and decoding processes. It also outlines Moody's prescriptive theory, which proposes seven principles for designing visual notations, including principles of semiotic clarity, perceptual discriminability, semantic transparency, and complexity management. The principles are intended to guide the development of visual notations that clearly and efficiently convey semantic meaning.

1. The “Physics” of Notations: Toward a
Scientific
Basis for Constructing Visual Notations
in Software Engineering
Daniel L. Moody, Member, IEEE
Presented By: Raj Kumar Ranabhat
M.E. In Computer Engineering, I/I
Kathmandu University

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Table of Content:
1. INTRODUCTION
2. RELATED RESEARCH
3. DESCRIPTIVE THEORY : HOW VISUAL NOTATIONS
COMMUNICATE
4. PRESCRIPTIVE THEORY : PRINCIPLES FOR DESIGNING
EFFECTIVE VISUAL NOTATIONS
5. CONCLUSION

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1. INTRODUCTION
1.1 The Nature of Visual Languages
1.2 The Dependent Variable: What Makes a “Good”
Visual Notation?
1.3 Visual Syntax: An Important but Neglected Issue
1.4 Why Visual Representation Is Important

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2. RELATED RESEARCH
2.1 Ontological Analysis

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3. DESCRIPTIVE THEORY : HOW VISUAL
NOTATIONS COMMUNICATE
3.1 Communication Theory

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3.2 The Design Space (Encoding Side)

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3.3 The Solution Space (Decoding Side)

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4. PRESCRIPTIVE THEORY : PRINCIPLES FOR
DESIGNING EFFECTIVE VISUAL NOTATIONS
4.1 Principle of Semiotic Clarity: There Should Be a 1:1
Correspondence between Semantic Constructs and Graphical
Symbols

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4.2 Principle of Perceptual Discriminability: Different
Symbols Should Be Clearly Distinguishable from Each
Other
4.2.1 Visual Distance
4.2.2 The Primacy of Shape
4.2.3 Redudant Coding
4.2.4 Textual Differentiation

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4.3 Principle of Semantic Transparency: Use Visual
Representations Whose Appearance Suggests Their
Meaning
4.3.1 Icons (Perceptual Resemblance)

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4.3.2 Semantically Transparent Relationships

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4.4 Principle of Complexity Management: Include Explicit
Mechanisms for Dealing with Complexity

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4.4.1 Modularization
4.4.2 Hierarchy (Levels of Abstraction)

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4.5 Principle of Cognitive Integration: Include Explicit
Mechanisms to Support Integration of Information from
Different Diagrams

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4.6 Principle of Visual Expressiveness: Use the Full Range
and Capacities of Visual Variables

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4.7 Principle of Dual Coding: Use Text to Complement
Graphics

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5. CONCLUSION
5.1 Practical Significance
5.2 Theoretical Significance
5.3 Limitations and Further Research
5.4 Wider Significance