Code and datasets

The current understanding of “feature implementation” largely stems from subjective opinions and a limited number of quantitative case studies, making it a blurred concept. We aim to address this gap by developing an objective data-driven method to explore feature implementation in high-quality open-source projects. Specifically, we analyze feature branches, issues, and pull requests to uncover patterns related to contributors, project management, communication, feature quality, and associated artifacts. As a first step, in this paper, we present our rationale for focusing on feature branches. We outline our data collection and analysis pipeline and share a few preliminary findings. Our work has direct practical implications for future feature development as it helps identify, understand, and promote best practices, help teams stay up to date with emerging development trends, optimize release cycles, identify bottlenecks in current feature development, identify effective contribution models, and improve collaboration.