  Background. During collaborative software development, developers often use
branches to add features or fix bugs. When merging changes from two branches,
conflicts may occur if the changes are inconsistent. Developers need to resolve
these conflicts before completing the merge, which is an error-prone and
time-consuming process. Early detection of merge conflicts, which warns
developers about resolving conflicts before they become large and complicated,
is among the ways of dealing with this problem. Existing techniques do this by
continuously pulling and merging all combinations of branches in the background
to notify developers as soon as a conflict occurs, which is a computationally
expensive process. One potential way for reducing this cost is to use a
machine-learning based conflict predictor that filters out the merge scenarios
that are not likely to have conflicts, ie safe merge scenarios. Aims. In this
paper, we assess if conflict prediction is feasible. Method. We design a
classifier for predicting merge conflicts, based on 9 light-weight Git feature
sets. To evaluate our predictor, we perform a large-scale study on 267, 657
merge scenarios from 744 GitHub repositories in seven programming languages.
Results. Our results show that we achieve high f1-scores, varying from 0.95 to
0.97 for different programming languages, when predicting safe merge scenarios.
The f1-score is between 0.57 and 0.68 for the conflicting merge scenarios.
Conclusions. Predicting merge conflicts is feasible in practice, especially in
the context of predicting safe merge scenarios as a pre-filtering step for
speculative merging.
