Self-admitted technical debts identification: How far are we?

• Main Authors: GU, Hao, ZHANG, Shichao, HUANG, Qiao, LIAO, Zhifang, LIU, Jiakun, LO, David
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: multi-task learning; Self-Admitted Technical Debt; MT-BERT-SATD; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/9260
• Institution: Singapore Management University

Self-admitted technical debt (SATD) is a kind of technical debt that is already acknowledged by the developers and needs additional work or resources to address in the future. In recent years, though many methods have been proposed to detect SATDs, these methods have mainly focused on Java-type code comments published by Maldonado et al. It is unclear whether these methods trained on Maldonado's code comments dataset can find SATD in other programming languages or other software artifacts, such as issue trackers, pull requests, and commit messages effectively. In order to answer the above confusion and investigate how far our community has progressed in the field of SATD identification, we first collect a comprehensive dataset that contains SATDs in code comments from four different programming languages (java, python, docker file, XML) and SATDs in other different artifacts (issue tracker, pull requests, commit messages) from previous papers working in the field of SATD. Then, we re-train the existing models with Maldonado's code comments dataset and test all the models on other programming languages and other artifacts. The results show that existing SATD identification methods can find SATDs in other non-Java languages, but perform poorly in identifying SATDs from three other different artifacts. In addition, in order to simultaneously identify four different artifacts of SATDs, we develop a Multi-Task Learning model utilizing BERT for SATD identification (MT-BERT-SATD). Considering four different artifacts and the SATD identification tasks, MT-BERT-SATD achieves an average F1-score of 0.712 (0.625-0.859), which is superior to existing models from 4.6% to 30.4%. Results show that MT-BERT-SATD can effectively identify SATD instances across explored programming languages and software artifacts, indicating its capability to identify SATD instances in new and unexplored programming languages and software artifacts.