Neural network based detection of self-admitted technical debt: From performance to explainability

• Main Authors: REN, Xiaoxue, XING, Zhenchang, XIA, Xin, LO, David, WANG, Xinyu, GRUNDY, John
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Self-admitted technical debt; Convolutional Neural Network; Cross project prediction; Model explainability; Model generalizability; Model adaptability; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4476; https://ink.library.smu.edu.sg/context/sis_research/article/5479/viewcontent/NN_based_classification_technical_debt_tosem_2019_av.pdf
• Institution: Singapore Management University

Technical debt is a metaphor to reflect the tradeoff software engineers make between short term benefitsand long term stability. Self-admitted technical debt (SATD), a variant of technical debt, has been proposed to identify debt that is intentionally introduced during software development, e.g., te...