Generative Analysis: The Power of Generative AI for Object-Oriented Software Engineering with UML (Video Course)

Description

6+ Hours of Video Instruction

Master Generative Analysis to build better software with UML and Generative AI by working at the right level of abstraction.

Generative AI is reshaping software engineering. As large language models like ChatGPT, Copilot, and Gemini generate code, your advantage shifts to analysis--clearly defining what the software must do at the right level of abstraction. This course teaches Generative Analysis, a practical, repeatable approach that bridges business analysis and engineering to produce high-quality inputs for LLMs and reliable outputs for your systems.

You learn how to make sound decisions about abstraction, model with UML, apply the Unified Process in an AI-augmented workflow, and capture information with structured techniques. You also use Literate Modeling to create accessible, narrative-rich specifications and M++ to fact-check and refine AI output using precise language patterns and multivalent logic. Through the OLAS example project, you practice prompt engineering, concept and dialog mapping, use case modeling, class and architecture design, and requirements processing--hands-on. The result: faster analysis, safer code generation, more reliable models, and more efficient delivery.

Learn How To

• Use Generative AI with UML to generate, validate, and iterate software engineering artifacts
• Identify and work at the optimal level of abstraction for LLM-powered analysis and code generation
• Apply Literate Modeling and M++ to capture precise requirements and fact-check AI outputs

Who Should Take This Course

• Software engineers, developers, and QA analysts adopting Generative AI in their workflows
• Business analysts, product managers, and technical leads responsible for requirements and design
• Solution architects and systems analysts modeling complex domains with UML

Course Requirements

• Basic knowledge of UML and object-oriented concepts
• Familiarity with programming and the software development lifecycle
• Comfort using Generative AI tools (e.g., ChatGPT, Copilot, Gemini) for prompts and code assistance

Lesson Descriptions

Lesson 1: The Evolution of Software Engineering in the Age of Generative AI
This lesson explores how LLMs change the balance between coding and analysis. You examine the Goldilocks level of abstraction for Generative AI, learn where human decision-making adds the most value, and practice framing problem statements that lead to reliable, testable AI outputs. You leave with a clear understanding of when to use AI, where to apply UML and models, and how to avoid common pitfalls.

Lesson 2: Generative Analysis for Generative AI
In this lesson, you learn the core principles of Generative Analysis and how to define effective abstraction. Through prompt engineering experiments, you compare outputs across tools and apply the X Files principles to manage uncertainty. You practice generating code from models, using UML to structure problems, and summarizing findings into clear, reusable artifacts.

Lesson 3: Modeling in Generative Analysis
This lesson covers building robust analysis models with convergent engineering. You practice choosing effective abstraction in object-oriented analysis, evaluating your models for completeness and consistency, and iterating with evidence. You learn trade-offs in model granularity, how to align models with requirements, and how to use UML to make complex ideas scannable and actionable.

Lesson 4: Launching OLAS--The Example Project
This lesson introduces the OLAS project to ground your learning in a realistic domain. You apply the Unified Process (UP), understand core workflows and phases, and see how Generative AI supports inception and beyond. You plan the project, define the problem space, and produce early artifacts (vision, scope, assumptions) that set up efficient, low-risk iterations.

Lesson 5: Capturing Information in Generative Analysis: Part 1
In this lesson, you master practical techniques for capturing and structuring information. You use mind mapping to quickly organize ideas and concept mapping to clarify relationships. You work with propositions to express testable statements and practice integrating Generative AI into concept maps to accelerate discovery while maintaining precision.

Lesson 6: Capturing Information in Generative Analysis: Part 2
In this lesson, you advance your understanding of information capture with dialog mapping, identify meeting antipatterns, and apply structured writing to produce clear, navigable documents. You learn when to use each technique, how to facilitate productive mapping sessions, and how to translate informal discussions into formal artifacts that LLMs can use effectively.

Lesson 7: The OLAS Elaboration Phase
In this lesson, you move from inception to elaboration. You concept-map OLAS, create an initial class diagram, and introduce architectural thinking. You design a first-cut logical architecture using patterns like layering, evaluate trade-offs, and iterate to increase clarity and testability. You leave with model and architecture artifacts ready for AI-assisted exploration.

Lesson 8: Communication
In this lesson, you learn to improve communication across teams and with AI. You examine semiotics (signs and meaning) and ontology (structure of concepts) in software engineering and connect them to convergent engineering. You practice crafting messages and models that reduce ambiguity, improve alignment, and help LLMs produce outputs that match your intent.

Lesson 9: The Generative Analysis Model of Human Communication
This lesson shows how to develop a practical communication model tailored for Generative Analysis. You analyze presuppositions, cognitive dissonance, and information flow to diagnose misunderstandings. You learn to annotate conversations and prompts, improving human-to-human collaboration and human-to-AI interaction. Your outputs become clearer, more precise, and easier to verify.

Lesson 10: M++
In this lesson you learn M++, a meta-language for recovering precise, structured information from informal communication. You practice identifying deletion, generalization, and distortion; use propositional functions and presuppositions; and transform vague statements into testable requirements. You apply M++ to fact-check AI outputs and reduce hallucinations using multivalent logic.

Lesson 11: Literate Modeling
This lesson shows how to address the limits of visual-only models by integrating narrative and diagrams. You learn the structure of a literate model, produce a Business Context Document, and use Generative AI to generate diagrams and textual specifications. You practice aligning models, narrative, and evidence to create accessible, review-ready deliverables.

Lesson 12: Information in Generative Analysis
This lesson shows how to work with the Generative Analysis information model across conversations, resources, questions, ideas, propositions, terms, and requirements. You learn how to process each type, write high-quality propositions backed by evidence, and refine requirements for clarity and testability. You practice using LLMs to capture and organize information without losing rigor.

Lesson 13: Generative Analysis by Example
In this lesson, you learn to apply Generative Analysis end-to-end using the OLAS vision statement. You use semantic highlighting to find key terms, conduct key statement analysis, and manage scope by knowing when to stop. You publish structured results for team review and reuse, creating a repeatable approach for future projects.

Lesson 14: OLAS Use Case Modeling
In this lesson, you create and present the initial use case model for OLAS. You handle homonyms to prevent misinterpretation, avoid common modeling mistakes, and structure use cases for realization. You produce a clear, navigable set of use case diagrams and specifications that feed class, sequence, and state modelsand accelerate AI-assisted prototyping.

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