Search-Based and Goal-Oriented Refactoring Using Unfolding of Graph Transformation Systems

To improve automation and traceability of search-based refactoring, in this thesis we propose a formulation of using graph transformation, where graphs represent object-oriented software architectures at the class level and rules describe refactoring operations. This formalisation allows us to make use of partial order semantics and an associated analysis technique, the approximated unfolding of graph transformation systems. In the unfolding we can identify dependencies and conflicts between refactoring steps leading to an implicit and therefore more scalable representation of the search space by sets of transformation steps equipped with relations of causality and conflict. To implement search based refactoring we make use of the approximated unfolding of graph transformation systems. An optimisation algorithm based on the Ant Colony paradigm is used to explore the search space, aiming to find a sequence of refactoring steps that leads to the best design at a minimal cost. Alternatively, we propose a more targeted approach, aiming at the removal of design flaws. The idea is that such sequences should be relevant to the removal of the flaw identified, i.e., contain only steps which are directly or indirectly contributes to the desired goal.