Comparative Study of Reinforcement Learning in GitHub Pull Request Outcome Predictions
In the rapidly evolving field of software development, pull-based development models, facilitated by tools such as GitHub, are essential for collaboration. This study explores factors that influence pull request (PR) outcomes and employs two Reinforcement Learning (RL) formalizations, modeled as Markov Decision Processes, for PR outcome prediction. The first model leverages 72 PR features and achieves a G-mean score of 0.82664, while the second focuses solely on PR discussions, resulting in a G-mean of 0.88372. Using a specially designed reward function, these RL formalizations strategically address data imbalance and excel in mimicking both single-stage and multi-stage PR review processes. They outperform baseline models (Random Forest, XGBoost, and a Naive Bayes baseline) across various data splits—namely 80/20, 50/50, and 20/80—and are particularly effective at predicting PR rejections. The study also makes its datasets publicly available for future research.
Thu 14 MarDisplayed time zone: Athens change
14:00 - 15:30 | |||
14:00 15mTalk | Exploring Markers and Drivers of Gender Bias in Machine Translations Research Papers Pre-print | ||
14:15 15mTalk | Delving into Parameter-Efficient Fine-Tuning in Code Change Learning: An Empirical Study Research Papers Shuo Liu City University of Hong Kong, Jacky Keung City University of Hong Kong, Zhen Yang Shandong University, Fang Liu Beihang University, Qilin Zhou City University of Hong Kong, Yihan Liao City University of Hong Kong | ||
14:30 15mTalk | Catch the Butterfly: Peeking into the Terms and Conflicts among SPDX Licenses Research Papers Liu Tao , Chengwei Liu Nanyang Technological University, Tianwei Liu School of Cyber Engineering, Xidian University, He Wang School of Cyber Engineering, Xidian University, Gaofei Wu School of Cyber Engineering, Xidian University, Yang Liu Nanyang Technological University, Yuqing Zhang University of Chinese Academy of Sciences; Zhongguancun Laboratory | ||
14:45 15mTalk | Comparative Study of Reinforcement Learning in GitHub Pull Request Outcome Predictions Research Papers | ||
15:00 15mTalk | On the Usefulness of Python Structural Pattern Matching: An Empirical Study Research Papers Norbert Vándor University of Szeged, Gabor Antal University of Szeged, Peter Hegedus University of Szeged, Rudolf Ferenc University of Szeged | ||
15:15 15mTalk | Deep Learning Model Reuse in the HuggingFace Community: Challenges, Benefit and Trends Research Papers Mina Taraghi Polytechnique Montréal, Gianolli Dorcelus Polytechnique Montréal, Armstrong Tita Foundjem Ecole Polytechnique de Montreal, Florian Tambon Polytechnique Montréal, Foutse Khomh Polytechnique Montréal Pre-print |