Identifying Self-admitted Technical Debt with Context-Based Ladder Network

Technical debt occurs when development teams take actions to expedite the delivery of a project at the cost of poor code quality and additional work of later refactoring. The accumulation of technical debt will make the software fixes prohibitively expensive. As a typical type of technical debt, Self-Admitted Technical Debt (SATD) is acknowledged by developers in code comments. Identifying SATD in code comments can improve code quality. However, manually discerning whether code comments contain SATD would be expensive and time-consuming. To solve this problem, we propose a method to apply the Ladder Network with the pre-training model to identify SATD based on the labeled data from 10 open source projects and the unlabeled data from another ten projects. By comparing with the original model of Ladder Network, and other semi-supervised learning models, the results show that the proposed method performs better in technical debt identification. In addition, the proposed method also achieves better results compared with supervised learning methods. This shows that our approach can make better use of unlabeled data to improve classification performance.