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

Learn Generative Analysis--a New Method of Object-Oriented Analysis--to Keep Pace with How Generative AI Is Transforming the Face of Software Engineering Generative AI is revolutionizing software engineering--many aspects of manual coding are becoming automated, and the skills needed by software engineers, developers, and analysts are evolving. Anyone who writes or works with code will need to produce precise analysis artifacts to feed the AI code-generation process. Enter generative analysis: a precise, structured way for software engineers, programmers, and analysts to transition to this new, AI-enhanced software engineering world. In Generative Analysis, experts Jim Arlow and Ila Neustadt leverage Literate Modeling, M++, and multivalent logic to lay out a step-by-step approach to object-oriented analysis that produces clear and unambiguous results suitable for further processing into code by generative AI systems such as Copilot, ChatGPT, and Gemini. Prepare for the challenge of the future by understanding the flexibility you already have at hand using generative analysis. Gain a new perspective on the shift to generative AI-based programming models Understand how generative analysis artifacts feed generative AIs to generate code and UML models Explore techniques that feed into and refine each other until a precise analysis definition of a software system is achieved Recognize milestones and end points to eliminate "analysis paralysis" Learn to work at the right level of abstraction to leverage the most power from generative AI Gain understanding from real-world, detailed examples of prompts and AI responses This guide teaches advanced, precise, and sophisticated analysis techniques that will allow you to thrive in the new world of software engineering with generative AI. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Table of Contents:
Preface xiv About the Authors xix Chapter 1: Generative Analysis for Generative AI 1 1.1 Introduction 1 1.2 Chapter contents 2 1.3 Communication and neuro linguistic programming (nlp) 3 1.4 Abstraction 7 1.5 Finding the right level of abstraction for Generative AI 14 1.6 Choice of Generative AI 14 1.7 Applying Generative AI to an example problem domain 15 1.8 Modeling in Generative Analysis 42 1.9 Chapter summary 51 Chapter 2: Launching OLAS, the example project 53 2.1 Introduction 53 2.2 Chapter contents 54 2.3 OLAS, the problem domain 55 2.4 Software engineering processes 55 2.5 The Unified Process (UP) 57 2.6 UP structure 60 2.7 UP workflows 62 2.8 UP phases 64 2.9 The UP phases in the world of Generative AI 68 2.10 The OLAS Inception phase 69 2.11 The OLAS Vision Statement 72 2.12 Keep all documents as concise as possible 73 2.13 Chapter summary 74 Chapter 3: Capturing information in Generative Analysis 77 3.1 Introduction 77 3.2 Chapter contents 78 3.3 Capturing informal, unstructured information 79 3.4 Mind mapping 82 3.5 Concept mapping 90 3.6 Dialog Mapping 107 3.7 Antipatterns in mapping meetings 114 3.8 Generative AI and mapping meetings 115 3.9 Structured writing 117 3.10 Structured documents 119 3.11 Principles for structuring information 120 3.12 Structured writing example 127 3.13 Complexity versus profundity 129 3.14 Chapter summary 132 Chapter 4: OLAS Elaboration phase 133 4.1 Introduction 133 4.2 Chapter contents 134 4.3 Concept-mapping OLAS 135 4.4 Creating a first-cut Logical Architecture 147 4.5 Using Generative AI to kick-start the OLAS Logical Architecture 151 4.6 How to validate the first-cut Logical Architecture 158 4.7 Chapter summary 158 Chapter 5: Communication 159 5.1 Introduction 159 5.2 Chapter contents 160 5.3 Communication in Generative Analysis 161 5.4 Flexibility is the key to excellent communication 162 5.5 Semiotics and the structure of meaning 164 5.6 Ontology 168 5.7 Metaphor 172 5.8 Constructing the Generative Analysis model of human communication 178 5.9 The Generative Analysis communication model 182 5.10 Chapter summary 187 Chapter 6: M++ 189 6.1 Introduction 189 6.2 Chapter contents 189 6.3 The nlp Meta Model and M++ 190 6.4 The M++ pattern template 192 6.5 Deletion 192 6.6 Generalization 209 6.7 Distortion 219 6.8 Presuppositions 235 6.9 Using M++ in Generative Analysis 239 6.10 Key points for applying M++ 240 6.11 Chapter summary 241 Chapter 7: Literate Modeling 243 7.1 Introduction 243 7.2 Chapter contents 244 7.3 Limitations of visual models as conveyors of meaning 245 7.4 The solution: Literate Modeling 247 7.5 Creating a Business Context Document (BCD) 249 7.6 Structure of the BCD 253 7.7 Learn Literate Modeling by example 255 7.8 Leveraging Generative AI for Literate Modeling 255 7.9 Integrating engineered prompts with BCDs 265 7.10 Chapter summary 266 Chapter 8: Information in Generative Analysis 267 8.1 Introduction 267 8.2 Chapter contents 268 8.3 Conversations with Generative AI 269 8.4 The Generative Analysis Information Model 271 8.5 Classifying information 274 8.6 Information 275 8.7 Resource 276 8.8 Question 277 8.9 Proposition 280 8.10 Idea 287 8.11 Requirement 288 8.12 Term 293 8.13 Chapter summary 297 Chapter 9: Generative Analysis by example 299 9.1 Introduction 299 9.2 Chapter contents 300 9.3 How to perform Generative Analysis 301 9.4 Identifying the Information Types 302 9.5 Semantic Highlighting 302 9.6 Finding Resources using Generative AI 304 9.7 Finding Terms 309 9.8 Key Statement analysis 316 9.9 Line-by-line Generative Analysis of the OLAS Vision Statement 321 9.10 Publishing your Generative Analysis results 326 9.11 Controlling the GA activity 326 9.12 Chapter summary 328 Chapter 10: OLAS use case modeling 331 10.1 Chapter contents 332 10.2 The first-cut use case model 333 10.3 Avoiding analysis paralysis in use case modeling 333 10.4 How to produce the first-cut use case model 334 10.5 Creating a use case model for OLAS 338 10.6 Using Generative AI in use case modeling 350 10.7 Patterns in use case modeling: CRUD 350 10.8 Structuring the use case model 351 10.9 The homonym problem 353 10.10 Common mistakes in use case modeling 358 10.11 Next steps in Generative Analysis of OLAS 359 10.12 Chapter summary 359 Chapter 11: The Administration subsystem 361 11.1 Introduction 361 11.2 Chapter contents 362 11.3 Elaborating the Administration subsystem 363 11.4 Writing CRUD use cases 364 11.5 Administration: Create 364 11.6 Administration: Read 383 11.7 Administration: Update 387 11.8 Administration: Delete 393 11.9 Administration use cases wrap-up 395 11.10 Use case realization for the Administration use cases 399 11.11 Creating a class diagram 417 11.12 Administration wrap-up 420 11.13 Generating a behavioral prototype 420 11.14 Chapter summary 433 Chapter 12: The Security subsystem 435 12.1 Introduction 435 12.2 Chapter contents 436 12.3 The Security subsystem 436 12.4 OLAS security policy 437 12.5 LogOn use case specification 439 12.6 UnfreezeAccount use case specification 445 12.7 LogOff use case specification 445 12.8 Use case realization for the Security subsystem 447 12.9 Creating sequence diagrams 448 12.10 Chapter summary 455 Chapter 13: The Catalog subsystem 457 13.1 Introduction 457 13.2 Chapter contents 459 13.3 The Normal and Restricted Collections 460 13.4 Modeling the Normal and Restricted Catalogs 461 13.5 The Type/Instance pattern 469 13.6 Type/Instance: Elements Similar for the OLAS catalogs 475 13.7 Creating a class model for the catalogs 476 13.8 The NormalCatalog subsystem use case model 486 13.9 Reuse with modification strategy for the RestrictedCatalog subsystem 504 13.10 The RestrictedCatalog subsystem use case model 505 13.11 Generative AI for use case realization 511 13.12 Catalog subsystem wrap-up 511 13.13 Chapter summary 514 Chapter 14: The Loan subsystem 515 14.1 Introduction 515 14.2 Chapter contents 516 14.3 Loan subsystem CRUD analysis 516 14.4 What is a loan? 517 14.5 Loan subsystem: Create 521 14.6 State machines for the Loan subsystem 530 14.7 Loan subsystem: Read 532 14.8 Fines 536 14.9 OLASUser class state machine 542 14.10 Loan subsystem: Update 546 14.11 Loan subsystem: Delete 546 14.12 Library vacations 551 14.13 LibraryVacation: Use case model 552 14.14 Trust no one 558 14.15 Loan subsystem wrap-up 564 14.16 Chapter summary 564 Chapter 15: The Innsmouth interface 567 15.1 Introduction 567 15.2 Chapter contents 567 15.3 Exchanging catalog information 568 15.4 How should the catalog sharing be handled in OLAS? 575 15.5 Updating the InnsmouthInterface use case model 577 15.6 Getting the Gilman Catalog 577 15.7 Generating the OLAS export mechanism for the restrictedCatalog 589 15.8 Innsmouth interface wrap-up 594 15.9 Chapter summary 594 Chapter 16: Milton++ 595 16.1 Introduction 595 16.2 Chapter contents 596 16.3 Communication trances 598 16.4 Rapport 601 16.5 Your unconscious mind 604 16.6 Trance and Generative AI 607 16.7 The Milton Model and Milton++ 615 16.8 Distortion, deletion, and generalization in Milton++ 616 16.9 Distortion 617 16.10 Deletion 622 16.11 Generalization 629 16.12 Chapter summary 634 Summary 635 Bibliography 637 Index 641