Transfer Learning for Improving Model Predictions in Highly Configurable Software
Transfer Learning for Improving Model Predictions in Highly Configurable Software
Modern software systems are built to be used in dynamic environments using configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we are often interested in reasoning about the performance of the systems under different configurations. Usually, we learn a black-box model based on real measurements …