MoCoRe - A Generic Model-Driven Composition and Rule-Based Refinement Framework

Modern software systems are constantly evolving and therefore subject to change. Model-based knowledge about software systems improves traceability, supports software evolution processes, and helps in quality prediction. Model transformation is often used to make heterogeneous model-based knowledge usable for model-consuming processes such as quality prediction. The goal of this work is to automate model-driven knowledge transformation and rule-based knowledge refinement to support model-consuming processes. Therefore, a model-driven composition and refinement approach is introduced that links model-generating processes such as reverse engineering with model-consuming processes such as quality prediction. The approach is realized in the form of a metamodel-independent framework that can be adapted to different target metamodels. The refinement rules of our approach are formulated using a high-level programming language. Besides the metamodel-independent framework, we present a concrete instantiation of the framework for a software architecture model for quality prediction. To demonstrate the approach, the instantiation of the framework is applied to six case studies. The results indicate that we can perform a lossless composition of input information into output models. Furthermore, the demonstration shows that the metamodel-independent framework enables knowledge refinement, achieving an F-score of 1.0 by enforcing eight specific refinement rules.