Developers regularly have to resolve merge conflicts, i.e., two conflicting sets of changes to the same files in different branches, which can be tedious and error-prone. To resolve conflicts, developers typically: keep the local version (KL) or the remote version (KR) of the code. They also sometimes manually edit both versions into a single one (ME). However, most existing techniques only support merging the local and remote versions (the ME strategy). We recently proposed RPRedictor, a machine learning-based approach to support developers in choosing how to resolve a conflict (by KL, KR, or ME), by predicting their resolution strategy. In its original design, RPRedictor uses a set of Evolution History Features ( s) that capture: the magnitude of the changes in conflict, their evolution, and the experience of the developers involved. In this paper, we proposed and evaluated a new set of Branch Edit Features ( s), that capture the fine-grained edits that were performed on each branch of the conflict. We learned multiple lessons. First, s provided lower effectiveness (F-score) than the original s. Second, combining s with s still did not improve the effectiveness of s, it provided the same f-score. Third, the feature set that provided highest effectiveness in our experiments was the combination of with a subset of s that captures the number of insertions performed in the local branch, but this combination only improved s by 3 pp. f-score. Finally, our experiments also share the lesson that some feature sets provided higher C-score (i.e., the safety of the technique’s mistakes) as a trade-off for lower f-scores. This may be valued by developers and we believe that it should be studied in the future.
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