Generative Analysis: The Power of Generative AI for Object-Oriented Software Engineering with UML by Pearson

Course details

Generative AI is reshaping software engineering. As models like ChatGPT, Copilot, Claude, and Gemini generate code, your competitive advantage shifts from coding to analysis—clearly defining what software must do at the right level of abstraction. This course outlines the essentials of 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. Learn how to make sound decisions about abstraction, model with UML, apply the Unified Process framework in an AI-augmented workflow, and use literate modeling to create narrative-rich specifications. Along the way, find out how to use M++ to fact-check and refine AI output using precise language patterns and multivalent logic. This course gives you a hands-on opportunity to practice prompt engineering, concept and dialog mapping, use case modeling, class and architecture design, requirements processing, and more.

Learning objectives
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.

Concepts

Introduction

Generative analysis

The Evolution of Software Engineering in the Age of Generative AI

Learning objectives
How software engineering is changing
Generative analysis evolution and purpose
Lesson summary

Generative Analysis for Generative AI

Learning objectives
Key principles of generative analysis
Defining abstraction
Code generation with generative AI
Prompt engineering experiments
The X Files principles
Using UML
Lesson summary

Modeling in Generative Analysis

Learning objectives
Convergent engineering
Effective abstraction in OO analysis
Evaluating your models
Lesson summary

Launching OLAS The Example Project

Learning objectives
Establishing the example problem domain
The unified process
Structure of the unified process
UP core workflows
UP phases and generative AI
Inception for OLAS
Lesson summary

Capturing Information in Generative Analysis - Part 1

Learning objectives
Information strategy
Mind mapping in software engineering
Concept mapping in software engineering
Working with propositions
Using generative AI with concept mapping
Lesson summary

Capturing Information in Generative Analysis - Part 2

Learning objectives
Four key techniques
Defining dialog mapping
Antipatterns and generative AI in mapping meetings
Using structured writing
Lesson summary

The OLAS Elaboration Phase

Learning objectives
The elaboration phase
Concept mapping OLAS
Creating the initial class diagram for OLAS
Architecture in software engineering
Creating the initial logical architecture for OLAS
Lesson summary

Communication

Learning objectives
Communication in software engineering
Semiotics in software engineering
Ontology in software engineering
Convergent engineering, semiotics, and ontology
Lesson summary

The Generative Analysis Model of Human Communication

Learning objectives
Developing a powerful model of communication in generative analysis
The generative analysis communication model
Lesson summary

M++

Learning objectives
Understanding M++ basics
Deletion
Generalization
Distortion
Propositional functions in M++
Presuppositions
How to effectively apply M++
Lesson summary

Literate Modeling

Learning objectives
Justifying the need for literate modeling
Structure of a literate model
Using generative AI for literate modeling
Lesson summary

Information in Generative Analysis

Learning objectives
Capturing with generative AI conversations
Processing information, resources, questions, and ideas
Propositions
Processing terms
Processing requirements
Lesson summary

Generative Analysis by Example

Applying generative analysis
Using semantic highlighting
Working with terms
Key statement analysis - A simple technique
Generative analysis of the OLAS vision statement
Knowing when to stop
Lesson summary
Learning objectives

OLAS Use Case Modeling

Learning objectives
How to work with homonyms
Common mistakes in use case modeling
Lesson summary
Creating an initial use case model
Presenting the initial use case model

Conclusion

Summary and next steps