Automating Change-level Self-admitted Technical Debt Determination
Self-Admitted Technical Debt (SATD) refers to technical debt that is introduced intentionally. Previous studies that identify SATD at the file-level in isolation cannot describe the TD context related to multiple files. Therefore, it is more beneficial to identify the SATD once a change is being made. We refer to this type of TD identification as “Change-level SATD Determination”, and identifying SATD at the change-level can help to manage and control TD by understanding the TD context through tracing the introducing changes. In this paper, we propose a change-level SATD Determination mode by extracting 25 features from software changes that are divided into three dimensions, namely diffusion, history and message, respectively. To evaluate the effectiveness of our proposed model, we perform an empirical study on 7 open source projects containing a total of 100,011 software changes. The experimental results show that our model achieves a promising and better performance than four baselines in terms of AUC and cost-effectiveness. On average across the 7 experimental projects, our model achieves AUC of 0.82, cost-effectiveness of 0.80, which is a significant improvement over the comparison baselines used. In addition, we found that “Diffusion” is the most discriminative dimension for determining TD-introducing changes.
Thu 8 NovDisplayed time zone: Guadalajara, Mexico City, Monterrey change
13:30 - 15:00 | Software Maintenance IIResearch Papers / Journal-First at Horizons 10-11 Chair(s): Emerson Murphy-Hill North Carolina State University | ||
13:30 22mTalk | Automating Change-level Self-admitted Technical Debt Determination Journal-First Meng Yan , Xin Xia Monash University, Emad Shihab Concordia University, David Lo Singapore Management University, Jianwei Yin , Xiaohu Yang DOI | ||
13:52 22mTalk | Large-Scale Study of Substitutability in the Presence of Effects Research Papers Jackson Maddox Iowa State University, USA, Yuheng Long Iowa State University, Hridesh Rajan Iowa State University | ||
14:15 22mTalk | An Empirical Study on Crash Recovery Bugs in Large-Scale Distributed Systems Research Papers Yu Gao Institute of Software, Chinese Academy of Sciences, Wensheng Dou Institute of Software, Chinese Academy of Sciences, Feng Qin Ohio State University, USA, Chushu Gao Institute of Software, Chinese Academy of Sciences, Dong Wang Institute of Software at Chinese Academy of Sciences, China, Jun Wei State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, Ruirui Huang Alibaba Group, China, Li Zhou Alibaba Group, China, Yongming Wu Alibaba Group, China | ||
14:37 22mTalk | Complementing Global and Local Contexts in Representing API Descriptions to Improve API Retrieval Tasks Research Papers Thanh Nguyen Iowa State University, Ngoc Tran , Hung Phan , Trong Nguyen Iowa State University, USA, Linh Truong , Trong Nguyen Iowa State University, USA, Hoan Anh Nguyen Iowa State University, USA, Tien N. Nguyen University of Texas at Dallas |