Explicit or Implicit? On Feature Engineering for ML-based Variability-intensive Systems

Abstract

Software variability engineering benefits from Machine Learning (ML) to learn e.g., variability-aware performance models, explore variants of interest and minimize their energy impact. As the number of applications of combining variability with ML grows, we would like to reflect on what is the core to the configuration process in software variability and inference in ML: feature engineering. These disciplines previously managed features explicitly, easing graceful combinations. Now, deep learning techniques derive automatically obscure but efficient features from data. Shall we give up explicit feature management in variability-intensive systems to embrace machine learning advances?