Context: The pull-based development model is widely used in open source projects, leading to the emergence of trends in distributed software development. One aspect that has garnered significant attention concerning pull request decisions is the identification of explanatory factors. Objective: This study builds on a decade of research on pull request decisions and provides further insights. We empirically investigate how factors influence pull request decisions and the scenarios that change the influence of such factors. Method: We identify factors influencing pull request decisions on GitHub through a systematic literature review and infer them by mining archival data. We collect a total of 3,347,937 pull requests with 95 features from 11,230 diverse projects on GitHub. Using these data, we explore the relations among the factors and build mixed effects logistic regression models to empirically explain pull request decisions. Results: Our study shows that a small number of factors explain pull request decisions, with that concerning whether the integrator is the same as or different from the submitter being the most important factor. We also note that the influence of factors on pull request decisions change with a change in context; e.g., the area hotness of pull request is important only in the early stage of project development, however it becomes unimportant for pull request decisions as projects become mature.@en

Pull-based development is a widely adopted paradigm for collaboration in distributed software development, attracting eyeballs from both academic and industry. To better study pull-based development model, this paper presents a new dataset containing 96 features collected from 11,230 projects and 3,347,937 pull re- quests. We describe the creation process and explain the features in details. To the best of our knowledge, our dataset is the most comprehensive and largest one toward a complete picture for pull-based development research.@en