Feature Changes in Source Code for Commit Classification Into Maintenance Activities

Abstract

Software maintenance plays an important role during software development and life cycle. Indeed, previous works show that maintenance activities consume most of the software budget. Therefore, understanding how these activities are performed can help software managers to previously plan and allocate resources in projects. Despite previous works, there is still a lack in accurate models to classify developers commits into maintenance activities. In the present article, we propose improvements in a state-of-the-art approach used to classify commits. Particularly, we include three additional features in the classification model and we use XGBoost, a boosting tree learning algorithm, for classification. Experimental results show that our approach outperforms the state-of-the-art baseline achieving more than 77% of accuracy and more than 64% in Kappa metric.