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Pull request (PR) based development, which is a norm for the social codingplatforms, entails the challenge of evaluating the contributions of, oftenunfamiliar, developers from across the open source ecosystem and, conversely,submitting a contribution to a project with unfamiliar maintainers. Previousstudies suggest that the decision of accepting or rejecting a PR may beinfluenced by a diverging set of technical and social factors, but often focuson relatively few projects, do not consider ecosystem-wide measures, or thepossible non-monotonic relationships between the predictors and PR acceptanceprobability. We aim to shed light on this important decision making process bytesting which measures significantly affect the probability of PR acceptance ona significant fraction of a large ecosystem, rank them by their relativeimportance in predicting PR acceptance, and determine the shape of thefunctions that map each predictor to PR acceptance. We proposed sevenhypotheses regarding which technical and social factors might affect PRacceptance and created 17 measures based on them. Our dataset consisted of470,925 PRs from 3349 popular NPM packages and 79,128 GitHub users who createdthose. We tested which of the measures affect PR acceptance and ranked thesignificant measures by their importance in a predictive model. Our predictivemodel had and AUC of 0.94, and 15 of the 17 measures were found to matter,including five novel ecosystem-wide measures. Measures describing the number ofPRs submitted to a repository and what fraction of those get accepted, andsignals about the PR review phase were most significant. We also discoveredthat only four predictors have a linear influence on the PR acceptanceprobability while others showed a more complicated response.

Effect of Technical and Social Factors on Pull Request Quality for the NPM Ecosystem