Model-Driven Reverse Engineering of Technology-Induced Architecture for Quality Prediction

In software engineering, techniques of continuous integration allow short test cycles and thus fast feedback cycles. However, if there is a need to change the deployment or update the functionality, it becomes difficult to assess the impact of such changes on performance. In this context, we present in this paper an extensible approach for reverse engineering of component-based systems for quality prediction. We build on the principles and techniques of model-driven engineering to analyze a system at the model level and to enable subsequent optimization.