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

TL;DR

This paper explores using Dynamic Symbolic Execution to analyze semantic differences in component-and-connector architectures, enhancing model comparison techniques with runtime data collection and evaluation of execution strategies.

Contribution

It introduces an enhanced MontiArc-to-Java generator for runtime data collection and evaluates DSE strategies for semantic difference analysis in component models.

Findings

DSE can provide valuable insights into system behavior differences.

Scalability is a significant challenge for applying DSE to large models.

The framework helps assess DSE's applicability in model-driven development.

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.