Revisiting supervised and unsupervised models for effort-aware just-in-time defect prediction

• Main Authors: HUANG, Qiao, XIA, Xin, LO, David
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Evaluation metrics; Defect prediction; Research bias; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4355; https://ink.library.smu.edu.sg/context/sis_research/article/5358/viewcontent/Revisting_effort_aware_JIT_DP_emse18_afv.pdf
• Institution: Singapore Management University

Effort-aware just-in-time (JIT) defect prediction aims at finding more defective software changes with limited code inspection cost. Traditionally, supervised models have been used; however, they require sufficient labelled training data, which is difficult to obtain, especially for new projects. Re...