Generative Analysis

Jim Arlow has been programming and designing object-oriented software systems since 1990. He has created object models for blue chip companies such as British Airways and M&G. He is a respected OO consultant in Europe and has written and delivered many training courses on object technology and Java. Jim is a regular speaker at conferences such as Object World, and has been an invited speaker at University College London, City University, and the British Computer Society.Ila Neustadt has worked in IT for British Airways (BA) for more than twenty years and has experience with all parts of the IT development life cycle. She worked in the strategy department modeling the architecture process and developing architecture training, and acted as program head for the graduate business analyst program. Ila now coordinates skills development for BA's IT staff.

• Preface xivAbout the Authors xixChapter 1: Generative Analysis for Generative AI 11.1 Introduction 11.2 Chapter contents 21.3 Communication and neuro linguistic programming (nlp) 31.4 Abstraction 71.5 Finding the right level of abstraction for Generative AI 141.6 Choice of Generative AI 141.7 Applying Generative AI to an example problem domain 151.8 Modeling in Generative Analysis 421.9 Chapter summary 51Chapter 2: Launching OLAS, the example project 532.1 Introduction 532.2 Chapter contents 542.3 OLAS, the problem domain 552.4 Software engineering processes 552.5 The Unified Process (UP) 572.6 UP structure 602.7 UP workflows 622.8 UP phases 642.9 The UP phases in the world of Generative AI 682.10 The OLAS Inception phase 692.11 The OLAS Vision Statement 722.12 Keep all documents as concise as possible 732.13 Chapter summary 74Chapter 3: Capturing information in Generative Analysis 773.1 Introduction 773.2 Chapter contents 783.3 Capturing informal, unstructured information 793.4 Mind mapping 823.5 Concept mapping 903.6 Dialog Mapping 1073.7 Antipatterns in mapping meetings 1143.8 Generative AI and mapping meetings 1153.9 Structured writing 1173.10 Structured documents 1193.11 Principles for structuring information 1203.12 Structured writing example 1273.13 Complexity versus profundity 1293.14 Chapter summary 132Chapter 4: OLAS Elaboration phase 1334.1 Introduction 1334.2 Chapter contents 1344.3 Concept-mapping OLAS 1354.4 Creating a first-cut Logical Architecture 1474.5 Using Generative AI to kick-start the OLAS Logical Architecture 1514.6 How to validate the first-cut Logical Architecture 1584.7 Chapter summary 158Chapter 5: Communication 1595.1 Introduction 1595.2 Chapter contents 1605.3 Communication in Generative Analysis 1615.4 Flexibility is the key to excellent communication 1625.5 Semiotics and the structure of meaning 1645.6 Ontology 1685.7 Metaphor 1725.8 Constructing the Generative Analysis model of human communication 1785.9 The Generative Analysis communication model 1825.10 Chapter summary 187Chapter 6: M++ 1896.1 Introduction 1896.2 Chapter contents 1896.3 The nlp Meta Model and M++ 1906.4 The M++ pattern template 1926.5 Deletion 1926.6 Generalization 2096.7 Distortion 2196.8 Presuppositions 2356.9 Using M++ in Generative Analysis 2396.10 Key points for applying M++ 2406.11 Chapter summary 241Chapter 7: Literate Modeling 2437.1 Introduction 2437.2 Chapter contents 2447.3 Limitations of visual models as conveyors of meaning 2457.4 The solution: Literate Modeling 2477.5 Creating a Business Context Document (BCD) 2497.6 Structure of the BCD 2537.7 Learn Literate Modeling by example 2557.8 Leveraging Generative AI for Literate Modeling 2557.9 Integrating engineered prompts with BCDs 2657.10 Chapter summary 266Chapter 8: Information in Generative Analysis 2678.1 Introduction 2678.2 Chapter contents 2688.3 Conversations with Generative AI 2698.4 The Generative Analysis Information Model 2718.5 Classifying information 2748.6 Information 2758.7 Resource 2768.8 Question 2778.9 Proposition 2808.10 Idea 2878.11 Requirement 2888.12 Term 2938.13 Chapter summary 297Chapter 9: Generative Analysis by example 2999.1 Introduction 2999.2 Chapter contents 3009.3 How to perform Generative Analysis 3019.4 Identifying the Information Types 3029.5 Semantic Highlighting 3029.6 Finding Resources using Generative AI 3049.7 Finding Terms 3099.8 Key Statement analysis 3169.9 Line-by-line Generative Analysis of the OLAS Vision Statement 3219.10 Publishing your Generative Analysis results 3269.11 Controlling the GA activity 3269.12 Chapter summary 328Chapter 10: OLAS use case modeling 33110.1 Chapter contents 33210.2 The first-cut use case model 33310.3 Avoiding analysis paralysis in use case modeling 33310.4 How to produce the first-cut use case model 33410.5 Creating a use case model for OLAS 33810.6 Using Generative AI in use case modeling 35010.7 Patterns in use case modeling: CRUD 35010.8 Structuring the use case model 35110.9 The homonym problem 35310.10 Common mistakes in use case modeling 35810.11 Next steps in Generative Analysis of OLAS 35910.12 Chapter summary 359Chapter 11: The Administration subsystem 36111.1 Introduction 36111.2 Chapter contents 36211.3 Elaborating the Administration subsystem 36311.4 Writing CRUD use cases 36411.5 Administration: Create 36411.6 Administration: Read 38311.7 Administration: Update 38711.8 Administration: Delete 39311.9 Administration use cases wrap-up 39511.10 Use case realization for the Administration use cases 39911.11 Creating a class diagram 41711.12 Administration wrap-up 42011.13 Generating a behavioral prototype 42011.14 Chapter summary 433Chapter 12: The Security subsystem 43512.1 Introduction 43512.2 Chapter contents 43612.3 The Security subsystem 43612.4 OLAS security policy 43712.5 LogOn use case specification 43912.6 UnfreezeAccount use case specification 44512.7 LogOff use case specification 44512.8 Use case realization for the Security subsystem 44712.9 Creating sequence diagrams 44812.10 Chapter summary 455Chapter 13: The Catalog subsystem 45713.1 Introduction 45713.2 Chapter contents 45913.3 The Normal and Restricted Collections 46013.4 Modeling the Normal and Restricted Catalogs 46113.5 The Type/Instance pattern 46913.6 Type/Instance: Elements Similar for the OLAS catalogs 47513.7 Creating a class model for the catalogs 47613.8 The NormalCatalog subsystem use case model 48613.9 Reuse with modification strategy for the RestrictedCatalog subsystem 50413.10 The RestrictedCatalog subsystem use case model 50513.11 Generative AI for use case realization 51113.12 Catalog subsystem wrap-up 51113.13 Chapter summary 514Chapter 14: The Loan subsystem 51514.1 Introduction 51514.2 Chapter contents 51614.3 Loan subsystem CRUD analysis 51614.4 What is a loan? 51714.5 Loan subsystem: Create 52114.6 State machines for the Loan subsystem 53014.7 Loan subsystem: Read 53214.8 Fines 53614.9 OLASUser class state machine 54214.10 Loan subsystem: Update 54614.11 Loan subsystem: Delete 54614.12 Library vacations 55114.13 LibraryVacation: Use case model 55214.14 Trust no one 55814.15 Loan subsystem wrap-up 56414.16 Chapter summary 564Chapter 15: The Innsmouth interface 56715.1 Introduction 56715.2 Chapter contents 56715.3 Exchanging catalog information 56815.4 How should the catalog sharing be handled in OLAS? 57515.5 Updating the InnsmouthInterface use case model 57715.6 Getting the Gilman Catalog 57715.7 Generating the OLAS export mechanism for the restrictedCatalog 58915.8 Innsmouth interface wrap-up 59415.9 Chapter summary 594Chapter 16: Milton++ 59516.1 Introduction 59516.2 Chapter contents 59616.3 Communication trances 59816.4 Rapport 60116.5 Your unconscious mind 60416.6 Trance and Generative AI 60716.7 The Milton Model and Milton++ 61516.8 Distortion, deletion, and generalization in Milton++ 61616.9 Distortion 61716.10 Deletion 62216.11 Generalization 62916.12 Chapter summary 634Summary 635Bibliography 637Index 641