Improved Conflict Detection for Graph Transformation with Attributes

TL;DR

This paper presents a refined static conflict detection method for graph transformations with attributes, explicitly considering attribute operation semantics to reduce false positives and ensure completeness.

Contribution

It introduces an improved conflict detection technique based on symbolic graphs that accounts for attribute operation semantics, enhancing precision over existing methods.

Findings

The method is proven complete, detecting all potential conflicts.

It reduces false positives compared to previous approaches.

The approach effectively handles attribute semantics in conflict detection.

Abstract

In graph transformation, a conflict describes a situation where two alternative transformations cannot be arbitrarily serialized. When enriching graphs with attributes, existing conflict detection techniques typically report a conflict whenever at least one of two transformations manipulates a shared attribute. In this paper, we propose an improved, less conservative condition for static conflict detection of graph transformation with attributes by explicitly taking the semantics of the attribute operations into account. The proposed technique is based on symbolic graphs, which extend the traditional notion of graphs by logic formulas used for attribute handling. The approach is proven complete, i.e., any potential conflict is guaranteed to be detected.