Model Based System Engineering (eBook)

Theoretical Foundations

Ali Koudri (Autor)

eBook Download: EPUB

2025
568 Seiten
Wiley (Verlag)
978-1-394-34508-3 (ISBN)

Infos

Well-structured and interdisciplinary overview of MBSE, covering both theoretical foundations and practical applications

Taking an interdisciplinary approach, Model Based System Engineering provides a comprehensive introduction to understanding and applying model-based system engineering (MBSE) principles and practices in the design, development, and management of complex systems. Throughout the book, readers will find case studies, practical examples and exercises, and multiple-choice questions that reinforce key concepts and promote active learning.

The book begins by exploring the historical context of MBSE, highlighting its emergence as a response to the limitations of traditional document-centric approaches. It emphasizes the crucial role of abstraction in MBSE and introduces key concepts, definitions, and taxonomies that form the bedrock of this discipline. Subsequent chapters delve into the core principles of modeling, examining the intricate relationships between systems, languages, and models.

Sample topics covered in Model Based System Engineering include:

• Prefaced by Bran Selic, a world authority on MBSE and software engineering
• Model verification and validation, exploring various techniques, such as model checking, simulation, and testing that enable the early detection and resolution of design errors and inconsistencies
• Model-based system architecting, methodological considerations, and application in real-world contexts
• Various modeling paradigms, including structural and behavioral models
• The pivotal role of languages in enabling effective modeling practices
• Benefits of formalization in enhancing the precision, consistency, and analyzability of system models

Model Based System Engineering is an essential resource for systems engineers, researchers, and students seeking to understand and harness the power of MBSE in tackling the complexities of modern systems.

Ali Koudri is a seasoned systems and software engineering expert with over 20 years of experience in designing and developing innovative solutions for complex systems. His extensive background spans diverse domains, including aeronautics, rail, defense, and healthcare, where he has consistently demonstrated his ability to tackle ambitious projects that require out-of-the-box thinking. Ali has held key roles in prominent organizations such as Thales Research and Technology and the Institute for Technological Research SystemX.

Textauszug

Chapter 1
The Start of the Journey

1.1 A Difficult Start

1.1.1 The Initial Foray

My experience with model-based systems engineering (MBSE) started more than 20 years ago. I was curious about the complex interplay between design and execution in the world of embedded real-time systems. With some knowledge of Unified Modeling Language (UML), I began this journey but soon encountered a gap between the theoretical models and the practical embedded code. Moving from high-level concepts to low-level implementation was more difficult than I had expected. Indeed, I did not expect this path would lead me to question systems engineering to such an extent, and that it would change the way I think about, design, and develop complex systems.

1.1.2 A Turning Point

In 2004, I went back to modeling, focusing on the development of interface code that seamlessly links software and hardware parts. The technical project I was working on definitely opened the doors to the MBSE field. Working on model transformations to produce C code from UML models, I began to understand the capabilities and limits of MBSE tools. This realization fueled my motivation to embark on a thesis aimed at closing the gap between abstract models and actual implementation.

1.1.3 Expanding the Scope

Writing my thesis was a valuable experience, even if it started with certain limitations. My initial efforts were mainly technical, relying on ideas that later seemed to limit my work. However, this stage of focused effort was crucial, pushing me to question my beliefs and deepen my understanding of MBSE.

Digging deeper, I discovered that MBSE is actually a complex concept, with interpretations varying depending on the application domain (Estefan et al. 2007). This insight prompted me to investigate the theoretical roots of MBSE, exploring areas such as philosophy, linguistics, epistemology, mathematics, and sociology. This multifaceted approach broadened my vision, going far beyond my original technical focus and showcasing the true complexity of this fascinating field.

1.1.4 Embracing the Journey

Reflecting on my journey, I realize that my early difficulties were not failures but opportunities for growth. They taught me the value of questioning my assumptions, considering different perspectives, and recognizing the interdisciplinary nature of MBSE. These insights provided me with a solid base for understanding the complexities of system design and using the full potential of model-based approaches. Although the journey was complex, it led me to a deeper appreciation of the transformative possibilities of MBSE.

1.2 The Power of Abstraction

1.2.1 The Need for a Paradigm Shift

My initial approach to MBSE was paved with good intentions, but quickly showed a significant mistake. Indeed, I focused on using models mainly to automate implementation and did not fully realize their true potential for reasoning and decision-making. The frequent mistakes and inconsistencies that came up during code execution highlighted the flaws in my approach. It became clear that the quality of the generated code depends greatly on the quality of the underlying design and specifications. I realized that code is not just a simple translation of requirements but a complex mixture of business needs and technical factors. It is this complex relationship that makes debugging very difficult, often requiring a return to the initial design to fix problems.

1.2.2 The Role of Abstraction

This reflection led me to consider the key importance of abstraction in MBSE. Building high-level, abstract models that capture the essence of the system is crucial. These models act as a guide, ensuring that specifications correctly represent the desired functions and behaviors, thus reducing confusion and promoting a shared understanding among stakeholders (Estefan et al. 2007; Friedenthal et al. 2014; Madni and Sievers 2018). The real challenge lies in gathering and understanding the real needs and requirements of stakeholders. Good communication and cooperation are essential in this process, and MBSE offers a framework to support this exchange by providing a common language and tools for requirements gathering, analysis, and validation.

By considering models as tools for reasoning and decision-making, MBSE encourages a more interactive and cooperative approach to systems thinking and engineering. Stakeholders are involved in the modeling process, providing feedback to refine specifications and ensure they meet their needs. This iterative process allows for the early finding and fixing of potential problems, reducing the risk of expensive rework later. The clarity and consistency within MBSE, maintained by a clear connection between models, specifications, and implementation, help to improve the understanding of the development lifecycle. The ability to track changes and refer to a single source of truth improves communication and understanding within the team.

1.2.3 The Modeling Benefits

Ultimately, my experience with MBSE showed that its real strength is not just in automation but in fostering better reasoning, decision-making, and communication throughout the systems engineering process. By emphasizing the creation of high-quality, abstract models that accurately represent stakeholder needs and expectations, MBSE enables the development of reliable, efficient, and purpose-driven systems. However, the success of MBSE relies on effective communication, collaboration, and a deep understanding of the problem. Only by focusing on these aspects can we truly realize the transformative potential of MBSE and create sustainable systems.

Figure 1.1 shows how abstraction works in MBSE with a comprehensive view of a library management system. This figure highlights how MBSE helps to understand the whole picture of complex systems, connecting abstract needs to actual implementations. The figure shows four interrelated areas: Need Space, Decision Space, Development Space, and Usage Space. These areas enable tracking the system development process from ideas to actual use.

Figure 1.1 Illustration of the power of abstraction in MBSE.

The Need Space captures the requirements for the library system. This view formalizes stakeholder needs and provides a clear starting point for the whole system engineering process.

The Decision Space aims to analyze design choices. By using different modeling representations, such as use case diagrams, class diagrams, and state machines, engineers can assess various alternatives and choose wisely those that best meet the needs of stakeholders. The iterative nature of modeling allows for early detection and fixing of potential problems, reducing costly rework later.

The Development Space explains how abstract models are translated into actual implementations. The figure includes database designs and code segments sourced from the concept models, highlighting MBSE’s ability to support model transformations. This maintains alignment between design and implementation, boosting the reliability of the system engineering.

Lastly, the Usage Space shows the system’s operational context, emphasizing that MBSE places usage at the heart of its concerns.

The two-way arrows between these spaces highlight the active and iterative nature of MBSE. They represent the continuous flow of information and feedback, allowing continuous improvement and adjustment of the system to changing needs and context.

By providing a shared language and a set of linked models, MBSE enhances communication and teamwork among different stakeholders. It creates a mutual understanding that transcends various domains, fostering joint reasoning about the system under study. Additionally, the traceability of this model-centric approach streamlines the analysis of impacts and decisions throughout the life of the system. Changes in one space can easily be traced, and their impacts assessed in other spaces, keeping the entire system aligned.

In summary, Figure 1.1 captures how MBSE uses abstraction to facilitate better reasoning, decision-making, and communication during the systems engineering process. By emphasizing high-quality models that reflect stakeholder needs and system behavior, MBSE leads to the development of systems that are reliable, efficient, and suitable for their intended use. This in-depth model-based approach greatly improves systems engineering, increasing system quality and reliability while promoting a more effective and cooperative development environment. The effectiveness of MBSE, as shown in this example, depends strongly on good communication, teamwork, and a deep and common understanding of the problem across all areas of system development.

Exercise: Identifying and resolving inconsistencies in system models

Examine the figure of the library management system, which includes use case diagrams, class diagrams, state machines, and database schemas.

• Identify discrepancies between models. For instance, ensure that all elements in the use case diagram are represented in the class diagram, if relevant.
• Verify that transitions in the state machine align with the operations defined in the class diagram.
• Confirm that the database schema supports the entities and relationships depicted in the class diagram.

Consider two levels of alignment in this diagram: cognitive alignment between your understanding and what you’ve expressed, and semantic alignment between what you’ve expressed and...

Produkt-Details

• Erscheint lt. Verlag: 10.9.2025
• Sprache: englisch
• Themenwelt: Technik ► Elektrotechnik / Energietechnik
• Schlagworte: Behavioral models • MBSE • Model Checking • modeling paradigms • model languages • model practices • model simulation • Model system • model techniques • Model Testing • model validation • model verification • structural models
• ISBN-10: 1-394-34508-9 / 1394345089
• ISBN-13: 978-1-394-34508-3 / 9781394345083