Skip to content
#large language models Book Open access

Skyscrapers Need Solid Foundations: Pattern-Based Validation of SysML v2 Models with Refinery

Oct 2026 · Proceedings of the ACM/IEEE 29th International Conference on Model Driven Engineering Languages and Systems · 0 citations · 7 references

TL;DR

The need for custom validation capabilities to ensure that models produced by both humans and LLMs are trustworthy enough to serve as the foundation of the MBSE ecosystem is addressed.

Abstract

The release of SysML v2, coinciding with rapid advances in Large Language Models (LLMs), marks a turning point for Model-Based Systems Engineering (MBSE). SysML v2 introduces a textual notation, a standard API suited for the Digital Thread, and a semantics grounded in a formal kernel, aiming to improve both syntactic and semantic interoperability between tools. These capabilities make it more important than ever to build valid and semantically consistent models. Although the SysML specification defines a large number of well-formedness constraints, it is still surprisingly easy to create semantically inconsistent models that can silently undermine the MBSE ecosystem built upon them. Emerging methodologies are also expected to introduce additional patterns, best practices, and modeling rules, which must be checked in addition to the built-in constraints. This paper addresses the need for custom validation capabilities to ensure that models produced by both humans and LLMs are trustworthy enough to serve as the foundation of the MBSE ecosystem. We present an approach that extracts the latest SysML metamodel from the official language sources in a separate preparation step, leverages the Systems Modeling API to retrieve models, and then uses the Refinery graph solver to detect undesired patterns defined in its declarative pattern language. The approach is illustrated using patterns from the "Common SysML v2 Pitfalls" blog series accompanying "The SysML v2 Book".

Read PDF

Similar papers

#computer vision Open access Jun 2016

Software Development in Startup Companies: The Greenfield Startup Model

The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.

Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al. · 178 citations · ⚡14
#computer vision Open access Oct 2016

Software Startups - A Research Agenda

Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.

M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al. · 157 citations · ⚡17
#machine learning Review Open access Oct 2016

“Failures” to be celebrated: an analysis of major pivots of software startups

This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.

Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al. · 127 citations · ⚡15
#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Conference Open access Dec 2013

Affordable and Energy-Efficient Cloud Computing Clusters: The Bolzano Raspberry Pi Cloud Cluster Experiment

The ongoing work building a Raspberry Pi cluster consisting of 300 nodes is presented, with potential use cases being an inexpensive and green test bed for cloud computing research and a robust and mobile data center for operating in adverse environments.

P. Abrahamsson, S. Helmer, Nattakarn Phaphoom et al. · 110 citations · ⚡7
#computer vision Book Open access Mar 2017

On the Unhappiness of Software Developers

The results indicate that software developers are a slightly happy population, but the need for limiting the unhappiness of developers remains, and 219 factors representing causes of unhappiness while developing software are identified.

D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al. · 84 citations · ⚡6

Related blog posts

MIT News · Artificial Intelligence Sep 14, 2026

New method enables AI for safety-critical situations

The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.

GPT-Lab Sep 10, 2026

Responsible AI Must Consider Its Afterlife

AI may appear weightless, but every model depends on physical infrastructure. To understand responsible AI, we need to look beyond algorithms and consider the entire lifecycle of the hardware behind them. The post Responsible AI Must Consider Its Afterlife appeared first on GPT-Lab.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.