Model Based Development

By Janet Six, Ph.D. on January 18, 2024

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Janet Six, Ph.D.

Janet Six, Ph.D.

Senior Product Manager

Understanding Model Based Development 

Model-Based Development (MBD) represents a paradigm shift in software and system development. Unlike traditional development methods that rely heavily on manual coding and scripting, Model Based Development emphasizes the use of high-level models and simulations as the primary means of developing complex systems, particularly in software engineering.

A model-based development visualization

A model-based development visualization.

It is driven by the philosophy that models can provide a more intuitive and comprehensive way of representing system requirements and behaviors than conventional programming languages. These models, often visual and sometimes mathematical, serve as the central artifacts in the development process. They offer a detailed blueprint of the system's functionality and architecture, which can then be automatically translated into executable code. This approach streamlines the development process, making it more efficient and less prone to errors.

Model Based Development's popularity is largely due to its ability to handle increasing system complexity while reducing development time and cost. In industries where safety and reliability are paramount, such as automotive and aerospace, it provides a structured approach to verifying and validating system behavior before any physical prototype is built. This early validation is crucial in identifying and addressing potential issues, thereby enhancing the overall quality and reliability of the final product.

Another key aspect of MBD is its focus on reusability and scalability. Models developed for one project can often be adapted or extended for others, saving significant time and resources. Moreover, it facilitates collaboration among different teams or even organizations by providing a clear and unambiguous representation of system functionalities and interfaces.

The Role of Models in MBD

In Model-Based Development, models are not just tools but the backbone of the entire development process. These models provide a high-fidelity, abstract representation of the system under development, encapsulating everything from functionality and behavior to specific architectural nuances.

A data-driven generated system model visualization

A data-driven generated system model visualization.

The primary role of models in Model Based Development is to serve as a single source of truth throughout the development lifecycle. Abstracting complex system functionalities into models enables developers and stakeholders to conceptualize and understand intricate system behaviors without delving into low-level code. This high-level abstraction is particularly beneficial in simplifying the development of complex systems, such as those found in the aerospace and automotive industries, where precision and accuracy are critical.

Furthermore, models in model based development facilitate early detection and correction of design flaws. By simulating and testing models, developers can identify potential issues early in the development cycle, long before physical prototypes are built or code is written. This preemptive identification of problems saves time and resources and significantly reduces the risk of costly errors later in the development process.

Models in MBD also enhance collaboration across diverse teams. With a shared visual representation of the system, different teams – be it engineering, design, or testing – can collaborate more effectively, ensuring a cohesive and unified approach to system development. This model based development aspect is particularly beneficial in large-scale projects involving multiple teams or organizations, where maintaining a consistent vision and understanding of the project can be challenging.

Advantages of Using Models in Software Development

Utilizing models in software development offers several distinct advantages that significantly enhance the overall development process. One of the primary benefits is the increased clarity and precision they provide. Models act as a visual and functional map of the system, offering a clear and comprehensive view of the software's architecture and behavior. This clarity is invaluable, especially when dealing with complex systems, as it aids in understanding and communicating the system's functionality among various stakeholders.

Another key advantage is the facilitation of early and efficient validation. With models, it's possible to simulate and test different aspects of the software early in the development cycle. This early testing ensures that potential issues are identified and addressed sooner, reducing the likelihood of costly fixes and rework at later stages.

Models also contribute to enhanced collaboration between different teams. Given their visual nature and high level of abstraction, models are more accessible and understandable to non-technical stakeholders. This accessibility fosters better communication and understanding across various departments, from development to business analysis, enhancing the overall quality of the final product.

Furthermore, the use of models in software development paves the way for automation. Many aspects of software design, testing, and even code generation can be automated using models, significantly speeding up the development process and reducing manual errors.

Key Differences Between MBD and Traditional Development

Model-Based Development differs significantly from traditional software development methodologies in several key aspects, revolutionizing how software is designed, developed, and maintained.

One of the fundamental differences is the central role of models in model based development. Unlike traditional methods, where coding is the primary activity, MBD focuses on creating and refining models as the main artifact. These models are not just visual aids but functional representations that can be directly translated into executable code. This shift from code-centric to model-centric development allows for a more intuitive and efficient process, especially for complex systems.

Another major difference lies in the approach to testing and validation. In traditional development, testing often occurs after significant portions of the code have been written, leading to the late discovery of defects. In contrast, model based development allows for continuous verification and validation throughout the development process. This ongoing testing, enabled by the models, helps identify and rectify issues much earlier, saving time and reducing costs associated with late-stage fixes.

Data interaction and navigation is key to effective model-based development

Data interaction and navigation is key to effective model-based development.


Also, it enhances collaboration and communication. Traditional development can often lead to silos, where different teams work independently with limited interaction. MBD's use of models as a common language bridges these gaps, facilitating better understanding and cooperation among various stakeholders, including developers, testers, and non-technical team members.

Furthermore, model based development often results in a more agile and adaptive development. Traditional methods can be rigid, making it challenging to accommodate changes late in the development cycle. MBD's flexible nature allows for easier model adjustments and updates, enabling a more responsive approach to evolving requirements and market demands.

Steps in Model Based Development

Requirements Analysis and Specification

The first critical step in Model-Based Development is the thorough analysis and specification of requirements. This stage involves understanding and documenting the functional and non-functional requirements of the system. In model based development, requirements are not just a checklist; they form the foundation upon which models are built. The accuracy and completeness of these requirements directly influence the effectiveness of the subsequent modeling process.

During this phase, stakeholders, including clients, end-users, and development teams, collaborate to delineate the system's intended behavior, performance criteria, and constraints. The key is to capture these requirements to be directly translated into models. This direct translation ensures that the models of the later stages reflect the stakeholders' needs and expectations.

Effective requirements analysis in model based development typically employs a variety of techniques, such as use case scenarios, user stories, and requirement workshops. These techniques help create a comprehensive and shared understanding of what the system is expected to achieve. This shared understanding is crucial for the success of the MBD process, as it guides the development of models that accurately reflect the desired system characteristics.


Modeling is the heart of Model-Based Development. This step transforms the requirements and specifications gathered earlier into detailed, structured models. These models represent various aspects of the system, including its behavior, structure, and interactions.

Types of Models in MBD

There are several types of models used in model based development, each serving a specific purpose:

  • Behavioral Models: These focus on the 'what' aspect of the system, describing the functional requirements and expected behaviors.
  • Structural Models: These define the 'how' aspect, outlining the system's architecture, components, and their interrelationships.
  • Data Models: These models represent the data structures and flows within the system.

Each type of model provides a different perspective on the system, and together, they offer a comprehensive view, ensuring all aspects are accurately represented and aligned with the requirements.

Model-Driven Architecture (MDA) in MBD

Model-Driven Architecture (MDA) is a key framework in model based development. It provides a set of guidelines for structuring specifications, which are expressed as models. It focuses on separating the specification of the system's functionality from its implementation on any specific technology platform. This separation allows for greater flexibility and portability, as the same model can be used to generate code for different platforms, reducing the need for rewriting or major alterations.

Model Verification and Validation

Once the models are developed, they must be rigorously verified and validated. This step ensures that the models accurately represent the requirements and are feasible. Verification checks if the models represent the desired functionality correctly, while validation ensures that these models meet the user's needs and expectations. Techniques like model simulation, review, and testing are used for this purpose.

Automatic Code Generation

A pivotal advantage of model-based development is the ability to generate code from the models automatically. This automated code generation is a huge time-saver and reduces human errors. The generated code can be used directly in the development process or as a robust foundation for further refinement.

Implementation and Testing

The final models serve as a guide for implementation. The automatically generated code or the insights from the models are used to build the actual system. Testing in model based development is iterative and continuous, aligning closely with the modeling process. The goal is to ensure that the final product faithfully represents the validated models, adhering to the specified requirements and functioning as intended.

This approach to modeling, verification, and implementation underpins its efficacy, making it a powerful methodology for modern software development.

Advantages of Model Based Development

Faster Software Development

One of the most significant advantages of Model-Based Development is accelerating the software development process. Using models as the primary artifact allows for rapid prototyping and iteration. The ability to simulate and test models quickly leads to faster feedback and refinement cycles. This rapid iteration significantly shortens the overall development time, enabling quicker delivery of functional software to the market.

Error Reduction and Improved Reliability

Model based development inherently improves the reliability and quality of software. The use of models and simulations allows for early detection and resolution of errors. Since models can be tested and validated before implementation, the likelihood of bugs and issues in the final code is substantially reduced. This proactive approach to error management leads to more robust and reliable software, crucial in industries where safety and precision are paramount.

Better Documentation and Maintenance

Models in model based development serve as excellent documentation tools. They represent the system's architecture and behavior, making it easier to understand and maintain the software over time. This enhanced documentation is particularly beneficial for long-term maintenance and future updates, as it allows new developers to grasp the system's workings quickly.

Challenges and Solutions

Need for Specialized Tools

Implementing model based development requires specialized modeling tools and environments. The long-term benefits, like reduced development time and increased software quality, often justify the initial investment. Additionally, the growing availability of MBD tools and resources makes it more accessible for a broader range of projects and organizations.

Team Training for Model Based Development 

The shift to model based development necessitates training and skill development for the team. This training is essential to leverage all the benefits effectively. Investing in comprehensive training programs ensures that team members are proficient in using modeling tools and techniques, paving the way for successful model based development implementation.

MBD Application in Different Industries

While model based development offers numerous benefits, its application varies across industries. Each industry has specific requirements and challenges to consider when adopting this development model. Customizing this approach to fit these unique industry needs is crucial for its successful application.

This detailed exploration of the advantages, challenges, and practical applications of Model-Based Development provides a comprehensive understanding of its impact and potential in the field of software development.

Applications of Model Based Development

In the Automotive Industry

Model-based development has revolutionized the automotive industry by enabling more efficient design and testing of complex vehicle systems. In this sector, it is used for designing advanced features like autonomous driving systems, safety mechanisms, and in-vehicle infotainment systems. Using models, automotive engineers can simulate and test different scenarios and conditions, ensuring the systems perform reliably in real-world situations. This approach speeds up the development process and enhances the safety and reliability of automotive products.

In the Aerospace Industry

Model based development plays a critical role in designing and verifying aircraft systems and components in aerospace. The high stakes of safety and performance in aerospace demand meticulous attention to detail, which is effectively managed through MBD. It allows for the simulation of flight conditions and behaviors, enabling engineers to refine systems for optimal performance and safety. This method significantly reduces the risks associated with physical testing and accelerates the development of innovative aerospace technologies.

In Medical Equipment

The medical equipment industry benefits from model based development through the precise and reliable development of medical devices. From imaging equipment to diagnostic and therapeutic devices, it helps ensure that these products meet stringent regulatory standards and function effectively in critical healthcare settings. By simulating various operating conditions and patient scenarios, medical device manufacturers can improve the safety and efficacy of their products.

In IoT (Internet of Things) Devices

MBD proves invaluable for IoT devices where software integration with hardware is vital. It allows developers to model the behavior of IoT devices in different environments and use cases, ensuring seamless functionality and connectivity. As IoT devices become more complex and ubiquitous, model based development provides a structured framework for managing this complexity and delivering reliable, high-quality IoT solutions.

Enhancing Model-Based Development with Tom Sawyer Perspectives

The utilization of advanced tools like Tom Sawyer Perspectives can significantly elevate the efficiency and effectiveness of the development process. Tom Sawyer Perspectives is a sophisticated software suite designed to assist in creating, visualizing, and analyzing complex models, which are central to model-based development.

Key Features of Perspectives application in MBD

  1. Automated Diagram Layout: Tom Sawyer Perspectives excels in automatically laying out diagrams into interactive, customized visualizations. This feature is invaluable in model based development, where clear and coherent representation of intricate system models is crucial for understanding and communication​​.
  2. User-Friendly Interface for Application Building: The software provides a point-and-click interface for building visualization applications, seamlessly integrating with standard development tools such as Git, Apache Maven, and Docker. This integration facilitates the efficient management of source control, builds, and continuous integration/continuous deployment (CI/CD) processes, streamlining the workflow in Model development environments​​.
  3. Complexity Management Tools: Managing the complexity of models in MBD is made easier with features such as model-based undo and redo, drill down, expansion, and nesting of model elements. These tools give developers greater control and flexibility, enabling them to handle complex systems more effectively​​.
  4. Seamless Data Integration: Perspectives supports integration with a wide range of data sources, making it easier to federate and integrate data from diverse origins. This capability ensures that models are built upon accurate and comprehensive data, enhancing the quality of the development process.
  5. Customization and Flexibility: The software allows extensive customization to meet specific needs within model based development. From altering visual styles to adapting layouts, Perspectives offers the flexibility needed to tailor the tool to various development requirements.

Why Adopt Model Based

Adopting Model-Based Development is a strategic decision that offers numerous benefits to software and system development organizations. It streamlines the development process, significantly reducing the time and resources needed to bring a product to market. It provides high precision and control, especially crucial in industries where safety and reliability are paramount.

Another compelling reason to adopt it is its ability to handle the increasing complexity of modern systems. As systems become more intricate, traditional development methods often fall short. MBD, emphasizing detailed modeling and early testing, is well-suited to manage this complexity effectively.

The shift to MBD also makes organizations more agile and responsive to changes. The flexibility of models allows for quick adaptations to new requirements or market demands. This agility is a significant advantage in today's fast-paced and ever-evolving technological landscape.

In conclusion, adopting Model-Based Development is a forward-thinking move that aligns with contemporary software and system development needs. It offers efficiency, accuracy, and flexibility, making it an attractive choice for organizations aiming to stay at the forefront of technological innovation.

About the Author

Janet Six, Ph.D. is Senior Product Manager at Tom Sawyer Software, where she works with business, technical, and design teams to help create effective data visualization and analysis solutions within technical, time, and financial constraints. She has been in the graph field for almost 30 years. Her work has appeared in the Journal of Discrete Algorithms, Journal of Graph Algorithms and Applications, and the Kluwer International Series in Engineering and Computer Science. The proceedings of conferences on Graph Drawing, IEEE Information Visualization, and Algorithm Engineering and Experiments have also included the results of her research. Her interests include graph visualization, analysis, and artificial intelligence.

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