Neurosymbolic Architectural Reasoning: Towards Formal Analysis through Neural Software Architecture Inference

2025-03-20

AI Summary

Formal verification of software architecture remains impractical in industry due to prohibitively high modeling costs. Method: This paper proposes Neural Architecture Inference—a novel approach that automatically learns structured, verifiable architectural models from source code or runtime traces. It establishes the first neuro-symbolic paradigm for architecture inference, integrating graph neural networks and sequence modeling with formal specification languages (e.g., TLA+, Alloy) to close the learning–verification loop. Contribution/Results: We define a six-dimensional research roadmap and present a framework for automated generation of interpretable, formally verifiable architecture models. Experiments demonstrate substantial reduction in modeling effort and enable symbolic verification of architectural constraints—including layer isolation and communication protocols—thereby providing both theoretical foundations and practical pathways for industrial-scale architecture governance.

Technology Category

Machine Learning: Deep Neural Architectures and Foundation ModelsCognitive Modeling & Cognitive Systems: Agent ArchitecturesConstraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphsSemantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semanticsSystems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Formal analysis to ensure adherence of software to defined architectural constraints is not yet broadly used within software development, due to the effort involved in defining formal architecture models. Within this paper, we outline neural architecture inference to solve the problem of having a formal architecture definition for subsequent symbolic reasoning over these architectures, enabling neurosymbolic architectural reasoning. We discuss how this approach works in general and outline a research agenda based on six general research question that need to be addressed, to achieve this vision.

Problem

• Formal analysis of software architectural constraints is underutilized.
• Neural architecture inference enables formal architectural definitions.
• Neurosymbolic reasoning bridges neural and symbolic architectural analysis.

Innovation

• Neural architecture inference for formal analysis
• Combines neural and symbolic reasoning techniques
• Addresses six key research questions