Graph transformations that change a graph while preserving consistency wrt a given set of constraints are an important type of transformation in many areas. For example, in search-based optimisation over graphs, aiming to find graph structures (e.g., models) that are optimal wrt some given objective functions, we need to be able to make changes to a graph without introducing violations of consistency constraints so that we can ensure to only generate feasible solutions. However, writing graph-transformation rules that ensure consistency-preservation while still allowing change to the graph is difficult: A change to the graph may introduce a constraint violation and, therefore, the rule must include components that repair any such new violations. Any such repair has the potential to introduce new constraint violations itself and, therefore, the number of potential repair paths to follow can quickly explode exponentially in size.
In this talk, I present work addressing these challenges in the context of optimal product-line configuration. We introduce a new data structure – feature-activation diagrams – to capture the dependencies between changes and use this to efficiently derive consistency-preserving graph transformation rules encoded as variability-based rules. The rules we generate allow us to solve existing benchmarks for product-line configuration more efficiently than the state of the art and find more optimal configurations.
Please note that the meeting will be recorded and live-streamed to YouTube: