Write a Blog >>
ASE 2020
Mon 21 - Fri 25 September 2020 Melbourne, Australia
Thu 24 Sep 2020 02:20 - 02:40 at Wombat - AI for Software Engineering (4) Chair(s): Hoa Khanh Dam

Self-Admitted Technical Debt (SATD) is a sub-type of technical debt. It is introduced to represent such technical debts that are intentionally introduced by developers in the process of software development. While being able to gain short-term benefits, the introduction of SATDs often requires to be paid back later with a higher cost, e.g., introducing bugs to the software or increasing the complexity of the software. To cope with these issues, our community has proposed various machine learning-based approaches to detect SATDs. These approaches, however, are either not generic that usually require manual feature engineering efforts or do not provide promising means to explain the predicted outcomes. To that end, we propose to the community a novel approach, namely HATD, to detect and explain SATDs using attention-based neural networks. Through extensive experiments on 445,365 comments in 20 projects, we show that HATD is effective in detecting SATDs on both in-the-lab and in-the-wild datasets under both within-project and cross-project settings. HATD also outperforms the state-of-the-art approaches in detecting and explaining SATDs.

Conference Day
Thu 24 Sep

Displayed time zone: (UTC) Coordinated Universal Time change

02:20 - 03:20
AI for Software Engineering (4)Research Papers / NIER track at Wombat
Chair(s): Hoa Khanh DamUniversity of Wollongong
Detecting and Explaining Self-Admitted Technical Debts with Attention-based Neural Networks
Research Papers
Xin WangWuhan University, Jin LiuWuhan University, Li LiMonash University, Australia, Xiao ChenMonash University, Xiao LiuSchool of Information Technology, Deakin University, Hao WuYunnan University
OCoR: An Overlapping-Aware Code Retriever
Research Papers
Qihao ZhuPeking University, Zeyu SunPeking University, Xiran LiangPeking University, Yingfei XiongPeking University, China, Lu ZhangPeking University, China
BugPecker: Locating Faulty Methods with Deep Learning on Revision Graphs
NIER track
Junming CaoSchool of Software, Shanghai Jiao Tong University, Shouliang YangSchool of Software, Shanghai Jiao Tong University, Wenhui JiangSchool of Software, Shanghai Jiao Tong University, Hushuang ZengSchool of Software, Shanghai Jiao Tong University, Beijun ShenSchool of Software, Shanghai Jiao Tong University, Hao ZhongShanghai Jiao Tong University