A large scale study of long-time contributor prediction for GitHub projects

• Main Authors: BAO, Lingfeng, XIA, Xin, LO, David, MURPHY, Gail C.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Long Time Contributor; GitHub; Prediction Model; Feature extraction; Task analysis; Numerical Analysis and Scientific Computing; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4359; https://ink.library.smu.edu.sg/context/sis_research/article/5362/viewcontent/Long_time_contributor_Github_tse191.pdf
• Institution: Singapore Management University

The continuous contributions made by long time contributors (LTCs) are a key factor enabling open source software (OSS) projects to be successful and survival. We study Github as it has a large number of OSS projects and millions of contributors, which enables the study of the transition from newcom...