Classifying Feature Models Maintainability based on Machine Learning Algorithms

Maintenance in the context of SPLs is a topic of interest, and that still needs further investigation. There are several ways to evaluate the maintainability of a feature model (FM), one of which is a manual or automated analysis of quality measures. However, the use of measures does not allow to evaluate the FM quality as a whole, as each measure considers a specific characteristic of FM. In general, the measures have wide ranges of values and do not have a clear definition of what is appropriate and inappropriate. In this context, the goal of this work is to investigate the use of machine learning techniques to classify the feature model maintainability. The research questions investigated in the study were: (i) how could machine learning techniques aid to classify FMs maintainability; and, (ii) which FM classification model has the best accuracy and precision. In this work, we proposed an approach for FM maintainability classification using machine learning technics. For that, we used a dataset of 15 FM maintainability measures calculated for 326 FMs, and we used machine learning algorithms to clustering. After this, we used thresholds to evaluate the general maintainability of each cluster. With this, we built 5 maintainability classification models that have been evaluated with the accuracy and precision metrics.