Abstract
Analyzing software maintenance activities is very helpful in ensuring cost-effective evolution and development activities. The categorization of commits into maintenance tasks supports practitioners in making decisions about resource allocation and managing technical debt.
In this paper, we propose to use a pre-trained language neural model, namely BERT (Bidirectional Encoder Representations from Transformers) for the classification of commits into three categories of maintenance tasks — corrective, perfective and adaptive. The proposed commit classification approach will help the classifier better understand the context of each word in the commit message.
We built a balanced dataset of 1793 labeled commits that we collected from publicly available datasets. We used several popular code change distillers to extract fine-grained code changes that we have incorporated into our dataset as additional features to BERT’s word representation features. In our study, a deep neural network (DNN) classifier has been used as an additional layer to fine-tune the BERT model on the task of commit classification. Several models have been evaluated to come up with a deep analysis of the impact of code changes on the classification performance of each commit category.
Experimental results have shown that the DNN model trained on BERT’s word representations and Fixminer code changes (DNN@BERT+Fix_cc) provided the best performance and achieved 79.66% accuracy and a macro-average f1 score of 0.8. Comparison with the state-of-the-art model that combines keywords and code changes (RF@KW+CD_cc) has shown that our model achieved approximately 8% improvement in accuracy. Results have also shown that a DNN model using only BERT’s word representation features achieved an improvement of 5% in accuracy compared to the RF@KW+CD_cc model.