MBSE Solutions & Software/System Modeling Tools: Abstraction & Architecture for the AI Era

Inside this Report

The complexity of today's embedded, edge, and AI systems demands new attention to engineering best practices. Organizations must identify the approaches required to drive innovation and manage change. Chief among those methods and tools is MBSE, a proven set of practices and technologies evolving to meet the needs of next-generation design requirements.

This report analyzes the market and emerging trends for standard language-based modeling (SLBM) tools (e.g., SysML/SysML v2, Modelica, etc.), as well as proprietary language-based modeling (PLBM) tools (e.g., SCADE, Simulink). It includes detailed discussion of emerging trends and technologies, standards and regulations, engineering behaviors, and competitive strategies that are impacting the market for MBSE solutions and software/system modeling tools.

What Questions are Addressed?

• How are needs for MBSE solutions evolving, and what are the key growth drivers through 2029?
• In what ways are increasing complexity, AI, and digital twin initiatives reshaping modeling tool need?
• What impact is SysML v2 having on the vendor landscape and MBSE utility?
• Which vertical markets are driving MBSE investment, and how do regulatory and certification requirements influence tool selection?
• How are leading vendors adapting their portfolios to AI and shifting lifecycle integration expectations?

Who Should Read this Report?

This research program is written for those making critical business decisions regarding product, market, channel, and competitive strategy and tactics. This report is intended for senior decision-makers who are developing embedded technology, including:

• CEO or other C-level executives
• Corporate development and M&A teams
• Marketing executives
• Business development and sales leaders
• Product development and strategy leaders
• Channel management and channel strategy leaders

Organizations Mentioned in this Report

• Altair Engineering
• Ansys
• Applied Dynamics International
• BigLever
• Black Duck
• BlackBerry (QNX)
• Bosch
• Cadence
• CodeSecure
• Dacoposte
• Dassault Systemes
• dSPACE
• Eclipse Foundation
• Emerson
• ETAS
• IAR
• IBM
• InQuery Labs
• Intland Software
• Jama Software
• MathWorks
• Mend
• Microsoft
• Modelix
• NI
• OpenText
• Papyrus (Eclipse)
• PTC
• pure-systems
• Red Hat
• Revenera
• Siemens
• Snyk
• Sonatype
• Sparx Systems
• Synopsys
• SysGit
• Vector Informatik
• Visual Paradigm International

Demand-side Research Overview

VDC launches numerous surveys of the IoT and embedded engineering ecosystem every year using an online survey platform. To support this research, VDC leverages its in-house panel of more than 30,000 individuals from various roles and industries across the world. Our global Voice of the Engineer survey recently captured insights from a total of 600 qualified respondents. This survey was used to inform our insight into key trends, preferences, and predictions within the engineering community.

Executive Summary

The overall market for MBSE and software/system modeling tools reached $B in 2024 and will reach $B in 2029, a CAGR of % over the forecast period, driven by strong growth within the embedded solution market. We believe this growth could accelerate even further in the coming years, as a function of both organic market need as well as further evangelism by the growing roster of PLM, EDA, and ALM companies all working to integrate more MBSE and SysML v2 solutions across their portfolios.

AI is redefining the needs of and opportunities for engineering organizations. Already, software and system modeling tool users are early adopters of AI across a range of use cases from end devices/systems integrating AI workloads to using AI within their own workflows. While many vendors are enhancing their ALM tools with AI-infused intelligence, there are two distinct use cases for AI system development for which modeling and MBSE are well suited. For one, SysML tools are ideal to help engineering organizations architect advanced systems and establish an underpinning for documentation and traceability for safety-critical projects. Proprietary language-based tools, such as MATLAB/Simulink and SCADE, can help organizations design, develop, and simulate systems with advanced algorithms and needs for real-time response to complex environmental, operational factors. We believe that the combination of advancing system complexity, safety-critical functionality requirements, and corporate mandates for efficiency will drive increasing need for sophisticated modeling tools and MBSE principles for years to come.

Key Findings

• Automotive remains the largest vertical for embedded software and system modeling tools. In recent years, market growth was driven by complexity and ECU proliferation, and the transition to centralized software-defined vehicle (SDV) programs. Now an increasing number of those programs need new systems of record and engagement given investments in autonomy, AI, and over-the-air software updates.
• SysML v2 will reshape and revitalize the standard language-based modeling tool market over the next three to five years. Although more tools are becoming available in the market, SysML v2 will still be a longer-term transition, as opposed to a short-term tool upgrade or shift cycle.
• Once viewed as a lower-cost and less-capable alternative to IBM's Rhapsody or PTC's Modeler, Dassault's commands the largest commercial market share for embedded SLBM tools.
• More than one-third of surveyed modeling tool users reported the integration of AI/ML into their end product. In fact, this rate is nearly twice that of the market at large and is expected to account for nearly two-thirds of modeling tool users in three years.
• For years, we have espoused the benefits of digital twin strategies and the foundational technologies required to enable them. MBSE solutions fit that use case directly. In fact, modeling tool users are twice as likely to establish digital twins in cloud services than those engineers not using modeling tools.

Code generation has been a key area of extension and value add for modeling tool vendors for over a decade. In practice, however, legacy solutions fell short due to shortcomings of architectural abstractions and the realities of fragmented hardware ecosystems. Despite generative AI coding capabilities only recently becoming widely commercially available, users of modeling tools have eagerly adopted these solutions at a disproportionately high rate, with % using the technology - a rate twice that of the industry overall.

Developers across both enterprise and embedded domains report significant reservations regarding the trustworthiness of AI-generated code. Across organization types, engineers identified code quality, security, compliance, and license infringement as leading concerns. Embedded engineers cited code quality as the absolute highest concern due to the importance of software performance in embedded system function. Software must run exactly as intended, regardless of deployment environment. Tool providers should restrict model training databases to ensure that AI generates reliable code based on tested documentation and examples, which will also help end users reduce licensing risks. In tandem, solution providers should offer model training and refinement as a service to further ensure a level of specialized code quality that generic LLM-based solutions cannot provide.

To address compliance concerns, modeling tool vendors should partner with requirements management, test, and software composition analysis (SCA) providers. Engineering organizations must effectively manage and trace

requirements to meet standards such as DO-178C and ISO 26262. IBM DOORS, Jama Connect, and Polarion from Siemens all help engineers track compliance from design to code to test. Similarly, SCA tools from vendors such as Black Duck, CodeSecure, Mend, Revenera, Sonatype, and Snyk track violations from known repositories to ensure that open source and AI-generated code do not violate existing licenses. In the same way that application lifecycle management, software testing, and SCA have converged in recent years to form single-platform solutions, AI code generation solutions and extensions fit directly within the software tooling landscape. A fully combined solution featuring modeling, requirements management, code generation, and software verification and validation would give customers a single dashboard or source of truth for code generation analytics, quality, induced risks, and impact on development time.