🤖 AI Summary
This work addresses the gap in formal methods research, which often relies on simplified examples and lacks accumulated, shared experience in modeling real-world complex systems. Focusing on the formal modeling process itself—not merely verification—the study systematically synthesizes key practical insights and lessons learned from large-scale case studies spanning networks, cyber-physical systems, hardware-software co-design, and biological systems. The project establishes a collection of reusable and comparable modeling exemplars grounded in real systems, thereby compensating for the omission of essential modeling details typically excluded from conventional publications due to space constraints. This repository of detailed models provides a robust foundation for future evaluation of formal methods and advancement of theoretical frameworks.
📝 Abstract
These proceedings contain the papers that were presented at the 7th Workshop on Models for Formal Analysis of Real Systems (MARS 2026), which took place on 12 April 2026 in Turin, Italy, as a satellite event of the 29th International Joint Conferences on Theory and Practice of Software (ETAPS 2026).
The goal of MARS is to bring together researchers from different communities who are developing formal models of real systems in areas where complex models occur (e.g., networks, cyber-physical systems, hardware/software codesign, biology). The motivation for MARS stems from the following two observations:
- Large case studies are essential to show that specification formalisms and modelling techniques are applicable to real systems, whereas many papers only consider toy examples or tiny case studies.
- Developing an accurate model of a real system takes a large amount of time, often months or years. In most papers, however, salient details of the model need to be skipped due to lack of space, and to leave room for formal verification methodologies and results.
MARS aims at remedying these issues, emphasising modelling over verification, so as to retain lessons learned from formal modelling, which are not usually discussed elsewhere, and which may lay the basis for future analysis and comparison.