HYDRA: Massively compositional model for cross-project defect prediction

• Main Authors: XIA, Xin, David LO, PAN, Sinno Jialin, NAGAPPAN, Nachiappan, WANG, Xinyu
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: Ensemble Learning; Cross-project Defect Prediction; Transfer Learning; Genetic Algorithm; Computer Sciences; Software Engineering; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/3415; https://ink.library.smu.edu.sg/context/sis_research/article/4416/viewcontent/HYDRA_Massively_2016_afv.pdf
• Institution: Singapore Management University

Most software defect prediction approaches are trained and applied on data from the same project. However, often a new project does not have enough training data. Cross-project defect prediction, which uses data from other projects to predict defects in a particular project, provides a new perspecti...