Leveraging Context Information for Self-Admitted Technical Debt Detection

Self-Admitted Technical Debt (SATD) refers to nonoptimal software design or implementation that is acknowledged and explicitly documented in the code by developers. Detecting SATD and understanding its evolution can help developers better manage their development activities and monitor the software quality. In recent years, numerous approaches have been proposed to automatically identify SATD. However, these approaches still suffer from a high number of false positives (i.e., non-SATD comments being detected as SATD). To further advance this field, in this paper, we conduct an empirical study to evaluate the performance of the state-of-theart SATD detection tools and investigate the causes behind the false positives. By manually analyzing 135 false positive cases, we identify the main types of comments that are easily misclassified. To address this issue, we propose a new approach, CASTI, which integrates context information into CodeBERT, a pre-trained model for programming languages. Our evaluation demonstrates that CASTI can significantly reduce the false positives and that the context information does help improve the performance.