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Existing research is unclear on how to generate lessons learned for defect prediction and effort estimation. Should we seek lessons that are global to multiple projects or just local to particular projects? This paper aims to comparatively evaluate local versus global lessons learned for effort estimation and defect prediction. We applied automated clustering tools to effort and defect datasets from the PROMISE repository. Rule learners generated lessons learned from all the data, from local projects, or just from each cluster.The results indicate that the lessons learned after combining small parts of different data sources (i.e., the clusters) were superior to either generalizations formed over all the data or local lessons formed from particular projects. We conclude that when researchers attempt to draw lessons from some historical data source, they should 1) ignore any existing local divisions into multiple sources, 2) cluster across all available data, then 3) restrict the learning of lessons to the clusters from other sources that are nearest to the test data.

Abstract-Data miners can infer rules showing how to improve either (a) the effort estimates of a project or (b) the defect predictions of a software module. Such studies often exhibit conclusion instability regarding what is the most effective action for different projects or modules.This instability can be explained by data heterogeneity. We show that effort and defect data contain many local regions with markedly different properties to the global space. In other words, what appears to be useful in a global context is often irrelevant for particular local contexts.This result raises questions about the generality of conclusions from empirical SE. At the very least, SE researchers should test if their supposedly general conclusions are valid within subsets of their data. At the very most, empirical SE should become a search for local regions with similar properties (and conclusions should be constrained to just those regions).

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