Improved Conflict Detection for Graph Transformation with Attributes
This work addresses a specific issue in graph transformation systems for researchers and practitioners, offering an incremental improvement in conflict detection techniques.
The paper tackles the problem of overly conservative conflict detection in graph transformation with attributes by proposing a less conservative condition that incorporates attribute operation semantics, and proves the approach is complete, guaranteeing detection of all potential conflicts.
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.