Model Engineering for Model-Driven Engineering

The effectiveness of model-driven engineering relies on our ability to build high-quality models. This task is intrinsically difficult. We need to produce sufficiently complete, adequate, consistent, and well-structured models from incomplete, imprecise, and sparse material originating from multiple, often conflicting sources. The system we need to consider in the early stages comprises software and environment components including people and devices. Such models should integrate the intentional, structural, functional, and behavioral facets of the system being developed. Rigorous techniques are needed for model construction, analysis, and evolution. They should support early and incremental reasoning about partial models for a variety of purposes, including satisfaction arguments, property checks, animations, the evaluation of alternative options, the analysis of risks, threats and conflicts, and traceability management. The tension between technical precision and practical applicability calls for a suitable mix of heuristic, deductive, and inductive forms of reasoning on a suitable mix of declarative and operational models. Formal techniques should be deployed only when and where needed, and kept hidden wherever possible. The talk will provide a retrospective account of our research efforts and practical experience along this route, including recent progress in model engineering for safety-critical medical workflows. Problem-oriented abstractions, analyzable models, and constructive techniques are pervasive concerns.