A model-driven framework for guided design space exploration

Design space exploration (DSE) aims at searching through various models representing different design candidates to support activities like configuration design of critical systems or automated maintenance of IT systems. In model-driven engineering, DSE is applied to find instance models that are (i) reachable from an initial model with a sequence of transformation rules and (ii) satisfy a set of structural and numerical constraints. Since exhaustive exploration of the design space is infeasible for large models, the traversal is often guided by hints, derived by system analysis, to prioritize the next states to traverse (selection criteria) and to avoid searching unpromising states (cut-off criteria). In this paper, we define an exploration approach where selection and cut-off criteria are defined using dependency analysis of transformation rules and an algebraic abstraction. The approach is evaluated against other exploration techniques and illustrated on a cloud infrastructure configuration problem.