An exploratory study on the introduction and removal of different types of technical debt in deep learning frameworks

• Main Authors: LIU, Jiakun, HUANG, Qiao, XIA, Xin, SHIHAB, Emad, LO, David, LI, Shanping
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Self-admitted technical debt; Deep learning; Categorization; Empirical study; Databases and Information Systems; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/6707; https://ink.library.smu.edu.sg/context/sis_research/article/7710/viewcontent/emse202.pdf
• Institution: Singapore Management University

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 techn...