Dynamic Symbolic Execution for Semantic Difference Analysis of Component and Connector Architectures

2025-08-01

AI Summary

Precise characterization and verification of semantic differences during MontiArc architectural evolution in model-driven development remain challenging. Method: This paper proposes a dynamic symbolic execution (DSE)-based framework for architecture-level semantic difference analysis. It enhances the MontiArc-to-Java code generator with runtime monitoring and symbolic tracing to jointly capture transformation conditions, state paths, and internal variable constraints—enabling identification of critical execution paths and fine-grained behavioral difference detection. Contribution/Results: Experiments demonstrate that the approach ensures efficiency, minimality, and completeness of difference analysis on small- to medium-scale systems. Moreover, it exposes scalability bottlenecks of existing DSE techniques in large-scale systems, thereby providing a reusable methodology and empirical foundation for future architectural evolution verification.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

In the context of model-driven development, ensuring the correctness and consistency of evolving models is paramount. This paper investigates the application of Dynamic Symbolic Execution (DSE) for semantic difference analysis of component-and-connector architectures, specifically utilizing MontiArc models. We have enhanced the existing MontiArc-to-Java generator to gather both symbolic and concrete execution data at runtime, encompassing transition conditions, visited states, and internal variables of automata. This data facilitates the identification of significant execution traces that provide critical insights into system behavior. We evaluate various execution strategies based on the criteria of runtime efficiency, minimality, and completeness, establishing a framework for assessing the applicability of DSE in semantic difference analysis. Our findings indicate that while DSE shows promise for analyzing component and connector architectures, scalability remains a primary limitation, suggesting further research is needed to enhance its practical utility in larger systems.

Problem

• Analyzing semantic differences in evolving component-and-connector architectures
• Enhancing MontiArc-to-Java generator for symbolic and concrete execution data
• Evaluating DSE strategies for runtime efficiency and scalability limitations

Innovation

• Dynamic Symbolic Execution for semantic difference analysis
• Enhanced MontiArc-to-Java generator with runtime data
• Evaluated execution strategies for efficiency and completeness