Background. Like traditional program code, software models are not resistant to change, but evolve over time by undergoing continuous extensions, corrections, and modifications. In model-driven engineering (MDE), evolution is multidimensional leading to the model management tasks of synchronization, versioning, and co-evolution. Whereas each of these tasks has recently received increased research interest, a systematic comparison and evaluation of the differ-ent approaches is missing. Within the FAME project, we aim at establishing a uniform framework characterizing changes and their impacts. The resulting findings will provide the basis for a suite of efficient techniques for avoiding unexpected side-effects of evolution. We will use different, well-explored formalisms with powerful inference engines exploiting con-cise semantics definitions of the modeling languages. By this, FAME will contribute to reliable change propagation indispensable for automatic quality assurance in MDE.

Research Objectives. FAME aims at improving model evolution mechanisms for various modelling languages in various model evolution scenarios.The goal of this project is the investigation of innovative methods preventing or at least detecting unintended side-effects of changes during the model-driven software development process. We direct our research efforts to wards a novel model management infrastructure which supports continuous integration in MDE. Continuous integration refers to a software development practice where each member of a team integrates his/her work frequently and where the integrated modifications are immediately verified in order to detect mistakes as quickly as possible. The shorter the time intervals are in which changes are integrated into the global project, the smaller is the chance that modifications cause malicious conflicts. The results are decreased development time and improved software quality.