A Novel Representation for Search-Based Model-Driven Engineering

Model-Driven Engineering (MDE) and Search-Based Software Engineering (SBSE) are development approaches that focus on automation to increase productivity and throughput. MDE focuses on high-level domain models and the automatic management of models to perform development processes, such as model validation or code generation. SBSE on the other hand, treats software engineering problems as optimisation problems, and aims to automatically discover solutions rather than systematically construct them. SBSE techniques have been shown to be beneficial at all stages in the software development life-cycle. There has, however, been few attempts at applying SBSE techniques to the MDE domain and all are problem-specific. In this thesis we present a method of encoding MDE models that enables many robust SBSE techniques to be applied to a wide-range of MDE problems. We use the model representation to address three in-scope MDE problems: discovering an optimal domain model; extracting a model of runtime system behaviour; and applying sensitivity analysis to model management operations in order to analyse the uncertainty present in models. We perform an empirical analysis of two important properties of the representation, locality and redundancy, which have both been shown to affect the ability of SBSE techniques to discover solutions, and we propose a detailed plan for further analysis of the representation, or other representations of its kind.