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Towards improving statistical modeling of software engineering data: think locally, act globally!
2014
Empirical Software Engineering
Much research in software engineering (SE) is focused on modeling data collected from software repositories. Insights gained over the last decade suggests that such datasets contain a high amount of variability in the data. Such variability has a detrimental effect on model quality, as suggested by recent research. In this paper, we propose to split the data into smaller homogeneous subsets and learn sets of individual statistical models, one for each subset, as a way around the high
doi:10.1007/s10664-013-9292-6
fatcat:b7qsfrf27vdu3e27akcgawaeqq