AI Assisted MBSE with SysML
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AI Assisted MBSE with SysML

An Integrated Systems/Software Approach

About the Book

Unlock the potential of Model-Based Systems Engineering (MBSE) and Artificial Intelligence in "AI Assisted MBSE with SysML: An Integrated Systems/Software Approach." Authored by experts Doug Rosenberg, Tim Weilkiens, and Brian Moberley, this book bridges systems and software engineering, offering a holistic approach to modern engineering challenges.

Explore the integration of AI and MBSE with SysML to enhance systems and software design processes. This book provides practical insights and methodologies for leveraging AI in MBSE, making it a resource for engineers, developers, and technical managers. Follow a comprehensive example, the design of a Scanning Electron Microscope, covering both hardware and software development from start to finish.

Key Features:

  • Comprehensive AI Integration: Understand how AI can be integrated into MBSE practices to boost efficiency and accuracy.
  • Practical Example: Follow a end-to-end case study of a Scanning Electron Microscope to see AI-assisted MBSE in action.
  • AI Personas: Learn how to interact with various AI personas specialized in different aspects of engineering.
  • SysML v1 and SysML v2: Get up-to-date with the latest advancements in SysML. 
  • Expert Insights: Benefit from the combined expertise of Doug Rosenberg, Tim Weilkiens, and Brian Moberley, each a thought leader in their field.
  • Systems Engineering: Requirements modeling, domain modeling, logical/physical architecture, parametric simulation.
  • Software Engineering: Use case development, code generation for microcontroller code, UI design/wireframing, DBMS integration, and testing.

About the Authors:

  • Doug Rosenberg, founder and CEO of Parallel Agile Inc., has over three decades of experience in software and systems engineering. A pioneer in object-oriented design and a leader in MBSE, Doug offers invaluable insights into integrating AI and systems engineering.
  • Tim Weilkiens, a prominent MBSE consultant, trainer, and member of the executive board of oose, has extensive involvement in the development of SysML and MBSE methodologies.
  • Brian Moberley, the Chief Model-Based Systems Engineer at STC, an Arcfield Company, is a leading expert in modeling and simulation. His expertise spans defense systems, commercial projects, and digital transformation.

Foreword by Dr. Azad M. Madni: Gain insights from a renowned expert in systems engineering and artificial intelligence as he highlights the importance of bridging systems and software engineering through AI.

About the Authors

Doug Rosenberg
Doug Rosenberg

Doug Rosenberg is the founder and CEO of Parallel Agile Inc., with over three decades of experience in software and systems engineering. As a pioneer in object-oriented design and a leader in MBSE, Doug brings invaluable insights into the integration of AI and systems engineering. AI Assisted MBSE is Doug's 9th book on software and systems engineering.

Doug provides training on AI Assisted MBSE (AIM Process) through Parallel Agile in both online and on-site formats.

More details about AIM Training are available on the parallelagile website

Tim Weilkiens
Tim Weilkiens

Tim is a member of the executive board of the German consulting company oose, an MBSE coach, and an active member of the OMG and INCOSE communities. He has written sections of the initial SysML specification and is a co-chair of the SysML v2 finalization task force.

As a coach, he has advised many companies in different domains. His insights into their challenges are one source of his experience that he shares.

Tim is a co-host of The MBSE Podcast.

All books written by Tim Weilkiens, including books not published by MBSE4U, can be found here.

You can contact him at tim@mbse4u.com and read his blog posts about MBSE in the MBSE4U blog.

Brian Moberley
Brian Moberley

Brian Moberley is a Chief Technologist at STC, an Arcfield Company. His Systems Engineering experience spans DoD as well as commercial projects. Brian is the creator and host of the MBSE iNsights YouTube channel where he creates instructional videos to teach the Systems Engineering community the ins and outs of the practice. As a veteran of the U.S. Air Force and commercial pilot, Brian's diverse background and experience bring a unique perspective to the Systems Engineering community.

Table of Contents

    • About Us
    • Foreword by Dr. Azad M. Madni
    • Preface
      • Reports of MBSE’s Death Have Been Greatly Exaggerated
      • AIM is an MBSE Process for Hardware/Software Co-Design
      • How to Read the Book
      • Acknowledgments
      • It’s a Matter of Common Sense
    • Brief Introduction to Artificial Intelligence
      • AI, ML, DL, GenAI, LLM, and ChatGPT
      • ChatGPT
      • How to talk with an AI
      • AI Subject Matter Experts
      • Let’s Start with the Main Matter
    Part I -Introduction
    • Chapter 1 -AI: A Game Changer for Software and Systems Engineering
      • 1.1Asking the Right Questions
      • 1.2AI Can Have Many Faces
      • 1.3AI Personas for Systems Engineering
      • 1.4Choosing a Stable Set of Abstractions
      • 1.5Without Software, There Is No System
      • 1.6How this Book is Organized
    • Chapter 2 -Without Software, There Is No System
      • 2.1Overview of SysML Behavior Models
      • 2.2Embedded vs Non-Embedded Software
      • 2.3What Kind of Code Can Be Generated from a SysML Model?
      • 2.4Each Type of Software Requires Its Own Prompt Set
      • 2.5Learning to converse with AI
      • 2.6Support for Domain Driven Design (DDD)
      • 2.7Support for Database Security
      • 2.8Support for Use Case Driven Development
      • 2.9Iterate Early, Iterate Often
      • 2.10Once the UI and Design Mesh, It’s Easy to Make It Look Good
      • 2.11Use Cases for MBSE Code Generation
      • 2.12Functional Decomposition vs Object-Oriented Design
      • 2.13Domain Driven Design
      • 2.14Many Systems Engineers Are Trained in IDEF0
      • 2.15Satire: The Resurgence of the Vampire
      • 2.16Process Roadmap for Hardware/Software Co-Design
      • 2.17Summing Up
    • Chapter 3 -Brief Introduction to SysML v2 and a Bit of AI
      • 3.1Kernel Modeling Language (KerML)
      • 3.2SysML v2 API and Services
      • 3.3Views and Diagrams
      • 3.4Graphical and Textual Notation
      • 3.5Definition and Usage
      • 3.6SysML v2 and AI
    Part II -Conceptual Modeling
    • Chapter 4 -AI-Assisted Domain Modeling
      • 4.1Domain Objects Form the Core of the Logical Architecture
      • 4.2Definition of Domain Model
      • 4.3Creating a Domain Model
      • 4.4Having Conversations with AI
      • 4.5Using AI to Help Us Develop a Domain Model
      • 4.6Writing Use Cases Helps Us Discover More Domain Objects
      • 4.7Zigzag Back and Forth from the Problem to the Solution
      • 4.8Sometimes AI is Forgetful
      • 4.9Sometimes AI Can Help You Cheat a Little
      • 4.10Preparing for Logical Architecture with Attributes and Operations
      • 4.11Let’s Do a SysML v2 Model
      • 4.12Summing Up
      • 4.13Prompts Used in this Chapter
    • Chapter 5 -AI-Assisted Use Case Modeling
      • 5.1Software/System Use Cases vs. Business Use Cases
      • 5.2Rainy Day Scenarios
      • 5.3Like Greased Lightning in a Bottle
      • 5.4Use Case Narratives are a Rich Source of Domain Objects
      • 5.5Can You Be a Little Less Verbose, Please?
      • 5.6Analysis Level Use Cases and Design Level Use Cases
      • 5.7Using Activities to Identify Alternate and Exception Behavior
      • 5.8Eliminating “Swiss Cheese” Requirements
      • 5.9More Precise Prompting Helps Get the Right Level of Detail
      • 5.10It’s Very Dangerous to Regard AI as Infallibly Correct
      • 5.11Close Encounter with an AI Hallucination
      • 5.12What About Software Use Cases?
      • 5.13Why Not Just Generate the Screens and Run Them?
      • 5.14Let’s Do a SysML v2 Model
      • 5.15Summing up
      • 5.16Prompts Used in this Chapter
    • Chapter 6 -AI-Assisted Requirements Modeling
      • 6.1Requirements Fundamentals
      • 6.2Starting with the Top-Level Requirements
      • 6.3Traceability is a Many-Splendored Thing
      • 6.4Requirements in Theory vs Requirements in Practice
      • 6.5Deep Dive Prompting
      • 6.6Measures of Effectiveness (MoE) Explained
      • 6.7154 Requirements Discovered with 8 Prompts
      • 6.8Let’s Do a SysML v2 Model
      • 6.9Summing Up
      • 6.10Prompts Used in this Chapter
    Part III -Logical and Physical Architecture
    • Chapter 7 -Subsystems and Logical Architecture
      • 7.1Advantages of a Domain-Driven Logical Architecture
      • 7.2Subsystems: Boundary Between Problem and Solution Space
      • 7.3Human-in-the-Loop Behavior Modeling
      • 7.4Simulating the Top-Level State Machine
      • 7.5Fleshing Out the Architecture: Diving into the Subsystems
      • 7.6Within Subsystems: State Machines
      • 7.7Let’s Do a SysML v2 Model
      • 7.8Overview of the Logical Architecture Process
      • 7.9Summing Up
      • 7.10Prompts Used in this Chapter
    • Chapter 8 -Components and Physical Architecture
      • 8.1Perry Matrix: From Persona to Agent
      • 8.2Start with a Subsystem BDD
      • 8.3Start by Choosing the Microcontroller
      • 8.4From Logical to Physical: Sensors and Amplifiers
      • 8.5Diving into Component Selection
      • 8.6Trade Studies
      • 8.7What About SysML v2?
      • 8.8Lather, Rinse, Repeat (Until All Components Are Selected)
      • 8.9Summing Up
      • 8.10Prompts Used in this Chapter
    • Chapter 9 -AI-Assisted Parametric Simulation
      • 9.1MOEs and Performance Requirements
      • 9.2Boost the Signal, Reduce the Noise
      • 9.3Perry Matrix Becomes an Agent and Gains a Purpose
      • 9.4Everything You Always Wanted to Know About SEM Imaging
      • 9.5Switching from a Noise Filter to a Pre-Amplifier
      • 9.6Setting Up the Simulation
      • 9.7Restoring Context Across Chat Sessions
      • 9.8Simulating a Signal Generator
      • 9.9Signal Generator Parametric Diagram
      • 9.10Result of the Simulation
      • 9.11Summing Up
      • 9.12Prompts Used in this Chapter
    Part IV -Software and Code Generation
    • Chapter 10 -Code Generation Before AI
      • 10.1SysML State Machines and Embedded Code
      • 10.2IBM Rhapsody
      • 10.3Embedded Engineer
      • 10.4Rhapsody vs Embedded Engineer
      • 10.5UI + Database Development: Low Code/No Code
      • 10.6CodeBot: Model-Driven Low Code Made Obsolete by AI
      • 10.7Summing Up
    • Chapter 11 -Generating Embedded Code from State Machines
      • 11.1Relationship Between Subsystems and State Machines
      • 11.2Microcontrollers
      • 11.3Tools for Real-Time Code Generation
      • 11.4Servo Magic
      • 11.5Getting Back to the Electron Microscope
      • 11.6Summary State Machines for All Subsystems
      • 11.7More Detail Available on Request
      • 11.8State Machine for Electron Beam Control
      • 11.9Taking a Top-Down Look
      • 11.10And Now, Here’s Something We Hope You’ll Really Like
      • 11.11Summing Up
      • 11.12Prompts Used in this Chapter
    • Chapter 12 -AI-Assisted Database Design and Programming
      • 12.1Some Fundamental Database Concepts
      • 12.2Database Requirements for the SEM
      • 12.3Introducing MERN Stack
      • 12.4A Simple Database for Storing and Retrieving Imagery
      • 12.5Setting up the Database (MongoDB)
      • 12.6Getting a Node.js Server Up and Running
      • 12.7Creating a Database Access API Using Node
      • 12.8Moving Towards a Database Access API
      • 12.9Refining the Database Access API
      • 12.10Generating Client-Side Database Access Objects
      • 12.11More Advanced Database Topics: Access Control
      • 12.12RBAC in Practice
      • 12.13Summing Up
      • 12.14Prompts Used in this Chapter
    • Chapter 13 -User Interface Design and Programming
      • 13.1Let’s Start by Listing the Screens for the SEM Software
      • 13.2The Simplified Spiral Model Is a Good Fit for UI Development
      • 13.3Starting from a Wireframe
      • 13.4Following the Evolutionary Spiral UI Process
      • 13.5Summing Up
      • 13.6Prompts Used in this Chapter
    • Chapter 14 -AI-Assisted Software Testing
      • 14.1Three Automatable Flavors of Software Testing
      • 14.2More Testing Flavors – Database and API Testing
      • 14.3One More Flavor: Hardware-in-the-Loop (HIL) Testing
      • 14.4Test Case Generation Part 1: Unit Testing
      • 14.5Test Case Generation Part 2: Behavior Testing
      • 14.6Test Case Generation 3: UI Testing
      • 14.7Electron Microscope: Unit Testing
      • 14.8Electron Microscope: BDD Testing
      • 14.9Electron Microscope: Selenium Testing
      • 14.10Electron Microscope: Database and API Test
      • 14.11Summing Up
      • 14.12Prompts Used in this Chapter
    Part V -Afterword
    • Chapter 15 -Afterword
    • Appendix A - SysML v2 Model for the SEM By Sister Mary Lou
      • Fig. 4.1 Initial Domain Model
      • Fig. 4.2 Expanded Domain Model
      • Fig. 5.1 SEM Use Cases
      • Figure 7.1 Subsystem Architecture
      • Figure 7.5 SEM Controller State Machine
      • Figure 7.6 Stage Subsystem
      • Figure 8.2 Imaging Subsystem
      • Figure 9.2 Imaging Subsystem Simulation Context
      • Figure 11.3 Controlling eBeam State Machine
    • References
    • Glossary
    • Index

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