  Background: Pull Request (PR) Integrators often face challenges in terms of
multiple concurrent PRs, so the ability to gauge which of the PRs will get
accepted can help them balance their workload. PR creators would benefit from
knowing if certain characteristics of their PRs may increase the chances of
acceptance. Aim: We modeled the probability that a PR will be accepted within a
month after creation using a Random Forest model utilizing 50 predictors
representing properties of the author, PR, and the project to which PR is
submitted. Method: 483,988 PRs from 4218 popular NPM packages were analysed and
we selected a subset of 14 predictors sufficient for a tuned Random Forest
model to reach high accuracy. Result: An AUC-ROC value of 0.95 was achieved
predicting PR acceptance. The model excluding PR properties that change after
submission gave an AUC-ROC value of 0.89. We tested the utility of our model in
practical scenarios by training it with historical data for the NPM package
\textit{bootstrap} and predicting if the PRs submitted in future will be
accepted. This gave us an AUC-ROC value of 0.94 with all 14 predictors, and
0.77 excluding PR properties that change after its creation. Conclusion: PR
integrators can use our model for a highly accurate assessment of the quality
of the open PRs and PR creators may benefit from the model by understanding
which characteristics of their PRs may be undesirable from the integrators'
perspective. The model can be implemented as a tool, which we plan to do as a
future work.
