Removal of Different Types of Technical Debt

To complete tasks faster, developers often have to sacrifice the quality of the software. Such compromised practice results in the increasing burden to developers in future development. The metaphor, technical debt, describes such practice. Prior research has illustrated the negative impact of technical debt, and many researchers investigated how developers deal with a certain type of technical debt. However, few studies focused on the removal of different types of technical debt in practice. To fill this gap, we use the introduction and removal of different types of self-admitted technical debt (i.e., SATD) in 7 deep learning frameworks as an example. This is because deep learning frameworks are some of the most important software systems today due to their prevalent use in life-impacting deep learning applications. Moreover, the field of the development of differJiakun Liu College of Computer Science and Technology, Zhejiang University, China PengCheng Laboratory, China E-mail: jkliu@zju.edu.cn Qiao Huang College of Computer Science and Technology, Zhejiang University, China E-mail: tkdsheep@zju.edu.cn Xin Xia Faculty of Information Technology, Monash University, Melbourne, Australia E-mail: xin.xia@monash.edu Emad Shihab Department of Computer Science and Software Engineering, Concordia University, Canada E-mail: eshihab@encs.concordia.ca David Lo School of Information Systems, Singapore Management University, Singapore E-mail: davidlo@smu.edu.sg Shanping Li College of Computer Science and Technology, Zhejiang University, China E-mail: shan@zju.edu.cn ar X iv :2 10 1. 03 73 0v 1 [ cs .S E ] 1 1 Ja n 20 21