Introduction
Generative analysis
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1. The Evolution of Software Engineering in the Age of Generative AI
How software engineering is changing
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Generative analysis evolution and purpose
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Lesson summary
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2. Generative Analysis for Generative AI
Learning objectives
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Key principles of generative analysis
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Defining abstraction
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Code generation with generative AI
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Prompt engineering experiments
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The X Files principles
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Using UML
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3. Modeling in Generative Analysis
Convergent engineering
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Effective abstraction in OO analysis
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Evaluating your models
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Lesson summary
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4. Launching OLAS—The Example Project
Establishing the example problem domain
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The unified process
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Structure of the unified process
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UP core workflows
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UP phases and generative AI
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Inception for OLAS
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5. Capturing Information in Generative Analysis: Part 1
Learning objectives
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Information strategy
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Mind mapping in software engineering
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Concept mapping in software engineering
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Working with propositions
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Using generative AI with concept mapping
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6. Capturing Information in Generative Analysis: Part 2
Learning objectives
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Four key techniques
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Defining dialog mapping
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Antipatterns and generative AI in mapping meetings
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Using structured writing
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7. The OLAS Elaboration Phase
Learning objectives
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The elaboration phase
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Concept mapping OLAS
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Creating the initial class diagram for OLAS
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Architecture in software engineering
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Creating the initial logical architecture for OLAS
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Lesson summary
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8. Communication
Learning objectives
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Communication in software engineering
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Semiotics in software engineering
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Ontology in software engineering
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Convergent engineering, semiotics, and ontology
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Lesson summary
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9. The Generative Analysis Model of Human Communication
Learning objectives
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Developing a powerful model of communication in generative analysis
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The generative analysis communication model
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Lesson summary
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10. M++
Learning objectives
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Understanding M++ basics
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Deletion
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Generalization
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Distortion
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Propositional functions in M++
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Presuppositions
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How to effectively apply M++
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11. Literate Modeling
Learning objectives
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Justifying the need for literate modeling
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Structure of a literate model
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Using generative AI for literate modeling
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Lesson summary
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12. Information in Generative Analysis
Learning objectives
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Capturing with generative AI conversations
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Processing information, resources, questions, and ideas
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Propositions
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Processing terms
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Processing requirements
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Lesson summary
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13. Generative Analysis by Example
Learning objectives
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Applying generative analysis
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Using semantic highlighting
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Working with terms
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Key statement analysis: A simple technique
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Generative analysis of the OLAS vision statement
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Knowing when to stop!
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Lesson summary
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14. OLAS Use Case Modeling
Learning objectives
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Creating an initial use case model
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Presenting the initial use case model
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How to work with homonyms
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Common mistakes in use case modeling
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Lesson summary
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Conclusion
Summary and next steps
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