Graph Transformation Planning via Abstraction

TL;DR

This paper presents a graph transformation planning approach for self-adaptive software systems, using abstraction-based heuristics to improve planning efficiency, validated through experiments in different domains.

Contribution

It introduces a novel planning system that operates directly on graph transformation systems with an abstraction-based heuristic, enhancing planning performance for self-adaptive systems.

Findings

The heuristic outperforms other domain-independent heuristics in experiments.

The approach effectively handles reconfiguration planning in self-adaptive software.

Experimental results demonstrate improved planning efficiency across domains.

Abstract

Modern software systems increasingly incorporate self-* behavior to adapt to changes in the environment at runtime. Such adaptations often involve reconfiguring the software architecture of the system. Many systems also need to manage their architecture themselves, i.e., they need a planning component to autonomously decide which reconfigurations to execute to reach a desired target configuration. For the specification of reconfigurations, we employ graph transformations systems (GTS) due to the close relation of graphs and UML object diagrams. We solve the resulting planning problems with a planning system that works directly on a GTS. It features a domain-independent heuristic that uses the solution length of an abstraction of the original problem as an estimate. Finally, we provide experimental results on two different domains, which confirm that our heuristic performs better than another domain-independent heuristic which resembles heuristics employed in related work.