Identification of Self-Admitted Technical Debt Using Enhanced Feature Selection Based on Word Embedding

Abstract

Self-admitted technical debt (SATD) is annotated in source code comments by developers and has been recognized as a great source of discovering flawed software. To reduce manual effort, some recent studies have focused on automated detection of SATD using text classification methods. To train their classifier, these methods need labeled samples, which also require a lot of effort to obtain. We developed a new SATD identification method, which takes advantage of a large corpus of unlabeled code comments, extracted from open source projects, to train a word embedding model. After applying feature selection, the pre-trained word embedding is used for discovering semantically similar features in source code comments to enhance the original feature set. By using such enhanced feature set for classification, our goal was to improve the identification of SATD when compared to existing methods. The proposed feature enhancement method was used with the three most common feature selection methods (CHI, IG, and MI), and three well-known text classification algorithms (NB, SVM, and ME) and was tested on ten open source projects. The experimental results show a significant improvement in SATD identification over the compared methods. With an achieved 82% of correct predictions of SATD, the proposed method seems to be a good candidate to be adopted in practice.