Home > Store

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

eBook

  • Your Price: $46.39
  • List Price: $57.99
  • Estimated Release: Jan 17, 2025
  • Includes EPUB and PDF
  • About eBook Formats
  • This eBook includes the following formats, accessible from your Account page after purchase:

    ePub EPUB The open industry format known for its reflowable content and usability on supported mobile devices.

    Adobe Reader PDF The popular standard, used most often with the free Acrobat® Reader® software.

    This eBook requires no passwords or activation to read. We customize your eBook by discreetly watermarking it with your name, making it uniquely yours.

Also available in other formats.

Register your product to gain access to bonus material or receive a coupon.

Description

  • Copyright 2025
  • Edition: 1st
  • eBook
  • ISBN-10: 0-13-829139-X
  • ISBN-13: 978-0-13-829139-6

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.

Extras

Author's Site

Please visit the author's site at clearviewtraining.com.

Sample Content

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

Updates

Submit Errata

More Information

InformIT Promotional Mailings & Special Offers

I would like to receive exclusive offers and hear about products from InformIT and its family of brands. I can unsubscribe at any time.

Overview


Pearson Education, Inc., 221 River Street, Hoboken, New Jersey 07030, (Pearson) presents this site to provide information about products and services that can be purchased through this site.

This privacy notice provides an overview of our commitment to privacy and describes how we collect, protect, use and share personal information collected through this site. Please note that other Pearson websites and online products and services have their own separate privacy policies.

Collection and Use of Information


To conduct business and deliver products and services, Pearson collects and uses personal information in several ways in connection with this site, including:

Questions and Inquiries

For inquiries and questions, we collect the inquiry or question, together with name, contact details (email address, phone number and mailing address) and any other additional information voluntarily submitted to us through a Contact Us form or an email. We use this information to address the inquiry and respond to the question.

Online Store

For orders and purchases placed through our online store on this site, we collect order details, name, institution name and address (if applicable), email address, phone number, shipping and billing addresses, credit/debit card information, shipping options and any instructions. We use this information to complete transactions, fulfill orders, communicate with individuals placing orders or visiting the online store, and for related purposes.

Surveys

Pearson may offer opportunities to provide feedback or participate in surveys, including surveys evaluating Pearson products, services or sites. Participation is voluntary. Pearson collects information requested in the survey questions and uses the information to evaluate, support, maintain and improve products, services or sites, develop new products and services, conduct educational research and for other purposes specified in the survey.

Contests and Drawings

Occasionally, we may sponsor a contest or drawing. Participation is optional. Pearson collects name, contact information and other information specified on the entry form for the contest or drawing to conduct the contest or drawing. Pearson may collect additional personal information from the winners of a contest or drawing in order to award the prize and for tax reporting purposes, as required by law.

Newsletters

If you have elected to receive email newsletters or promotional mailings and special offers but want to unsubscribe, simply email information@informit.com.

Service Announcements

On rare occasions it is necessary to send out a strictly service related announcement. For instance, if our service is temporarily suspended for maintenance we might send users an email. Generally, users may not opt-out of these communications, though they can deactivate their account information. However, these communications are not promotional in nature.

Customer Service

We communicate with users on a regular basis to provide requested services and in regard to issues relating to their account we reply via email or phone in accordance with the users' wishes when a user submits their information through our Contact Us form.

Other Collection and Use of Information


Application and System Logs

Pearson automatically collects log data to help ensure the delivery, availability and security of this site. Log data may include technical information about how a user or visitor connected to this site, such as browser type, type of computer/device, operating system, internet service provider and IP address. We use this information for support purposes and to monitor the health of the site, identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents and appropriately scale computing resources.

Web Analytics

Pearson may use third party web trend analytical services, including Google Analytics, to collect visitor information, such as IP addresses, browser types, referring pages, pages visited and time spent on a particular site. While these analytical services collect and report information on an anonymous basis, they may use cookies to gather web trend information. The information gathered may enable Pearson (but not the third party web trend services) to link information with application and system log data. Pearson uses this information for system administration and to identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents, appropriately scale computing resources and otherwise support and deliver this site and its services.

Cookies and Related Technologies

This site uses cookies and similar technologies to personalize content, measure traffic patterns, control security, track use and access of information on this site, and provide interest-based messages and advertising. Users can manage and block the use of cookies through their browser. Disabling or blocking certain cookies may limit the functionality of this site.

Do Not Track

This site currently does not respond to Do Not Track signals.

Security


Pearson uses appropriate physical, administrative and technical security measures to protect personal information from unauthorized access, use and disclosure.

Children


This site is not directed to children under the age of 13.

Marketing


Pearson may send or direct marketing communications to users, provided that

  • Pearson will not use personal information collected or processed as a K-12 school service provider for the purpose of directed or targeted advertising.
  • Such marketing is consistent with applicable law and Pearson's legal obligations.
  • Pearson will not knowingly direct or send marketing communications to an individual who has expressed a preference not to receive marketing.
  • Where required by applicable law, express or implied consent to marketing exists and has not been withdrawn.

Pearson may provide personal information to a third party service provider on a restricted basis to provide marketing solely on behalf of Pearson or an affiliate or customer for whom Pearson is a service provider. Marketing preferences may be changed at any time.

Correcting/Updating Personal Information


If a user's personally identifiable information changes (such as your postal address or email address), we provide a way to correct or update that user's personal data provided to us. This can be done on the Account page. If a user no longer desires our service and desires to delete his or her account, please contact us at customer-service@informit.com and we will process the deletion of a user's account.

Choice/Opt-out


Users can always make an informed choice as to whether they should proceed with certain services offered by InformIT. If you choose to remove yourself from our mailing list(s) simply visit the following page and uncheck any communication you no longer want to receive: www.informit.com/u.aspx.

Sale of Personal Information


Pearson does not rent or sell personal information in exchange for any payment of money.

While Pearson does not sell personal information, as defined in Nevada law, Nevada residents may email a request for no sale of their personal information to NevadaDesignatedRequest@pearson.com.

Supplemental Privacy Statement for California Residents


California residents should read our Supplemental privacy statement for California residents in conjunction with this Privacy Notice. The Supplemental privacy statement for California residents explains Pearson's commitment to comply with California law and applies to personal information of California residents collected in connection with this site and the Services.

Sharing and Disclosure


Pearson may disclose personal information, as follows:

  • As required by law.
  • With the consent of the individual (or their parent, if the individual is a minor)
  • In response to a subpoena, court order or legal process, to the extent permitted or required by law
  • To protect the security and safety of individuals, data, assets and systems, consistent with applicable law
  • In connection the sale, joint venture or other transfer of some or all of its company or assets, subject to the provisions of this Privacy Notice
  • To investigate or address actual or suspected fraud or other illegal activities
  • To exercise its legal rights, including enforcement of the Terms of Use for this site or another contract
  • To affiliated Pearson companies and other companies and organizations who perform work for Pearson and are obligated to protect the privacy of personal information consistent with this Privacy Notice
  • To a school, organization, company or government agency, where Pearson collects or processes the personal information in a school setting or on behalf of such organization, company or government agency.

Links


This web site contains links to other sites. Please be aware that we are not responsible for the privacy practices of such other sites. We encourage our users to be aware when they leave our site and to read the privacy statements of each and every web site that collects Personal Information. This privacy statement applies solely to information collected by this web site.

Requests and Contact


Please contact us about this Privacy Notice or if you have any requests or questions relating to the privacy of your personal information.

Changes to this Privacy Notice


We may revise this Privacy Notice through an updated posting. We will identify the effective date of the revision in the posting. Often, updates are made to provide greater clarity or to comply with changes in regulatory requirements. If the updates involve material changes to the collection, protection, use or disclosure of Personal Information, Pearson will provide notice of the change through a conspicuous notice on this site or other appropriate way. Continued use of the site after the effective date of a posted revision evidences acceptance. Please contact us if you have questions or concerns about the Privacy Notice or any objection to any revisions.

Last Update: November 17, 2020