DeepJIT: an end-to-end deep learning framework for just-in-time defect prediction

• Main Authors: HOANG, Thong, DAM, Hoa Khanh, KAMEI, Yasutaka, LO, David, UBAYASHI, Naoyasu
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Convolutional neural network; Deep learning; Just-in-time defect prediction; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4486; https://ink.library.smu.edu.sg/context/sis_research/article/5489/viewcontent/Thong_MSR2019.pdf
• Institution: Singapore Management University

Software quality assurance efforts often focus on identifying defective code. To find likely defective code early, change-level defect prediction – aka. Just-In-Time (JIT) defect prediction – has been proposed. JIT defect prediction models identify likely defective changes and they are trained using...