  In collaborative software development, multiple contributors frequently
change the source code in parallel to implement new features, fix bugs,
refactor existing code, and make other changes. These simultaneous changes need
to be merged into the same version of the source code. However, the merge
operation can fail, and developer intervention is required to resolve the
conflicts. Studies in the literature show that 10 to 20 percent of all merge
attempts result in conflicts, which require the manual developer's intervention
to complete the process. In this paper, we concern about a specific type of
change that affects the structure of the source code and has the potential to
increase the merge effort: code refactorings. We analyze the relationship
between the occurrence of refactorings and the merge effort. To do so, we
applied a data mining technique called association rule extraction to find
patterns of behavior that allow us to analyze the influence of refactorings on
the merge effort. Our experiments extracted association rules from 40,248 merge
commits that occurred in 28 popular open-source projects. The results indicate
that: (i) the occurrence of refactorings increases the chances of having merge
effort; (ii) the more refactorings, the greater the chances of effort; (iii)
the more refactorings, the greater the effort; and (iv) parallel refactorings
increase even more the chances of having effort, as well as the intensity of
it. The results obtained may suggest behavioral changes in the way refactorings
are implemented by developer teams. In addition, they can indicate possible
ways to improve tools that support code merging and those that recommend
refactorings, considering the number of refactorings and merge effort
attributes.
