Better Cross Company Defect Prediction

Fayola Peters, Tim Menzies, Andrian Marcus Abstract— How can we find data for quality prediction? Early in the lifecycle, projects may lack the data needed to build such predictors. Prior work assumed that relevant training data was found nearest to the local project. But is this the best approach? This paper introduces the Peters filter that is based on the following conjecture. When local project data is scarce, there is more information in other projects than locally. Accordinging, this filter selects training data via the structure of the other projects. We tested the Peters filter on 21 small data set looking for training data in 35 larger data sets. In the majority case (67%), the Peters filter builds much better defect predictors that the current-state-of-the-art methods. Hence, we recommend the Peters filter for cross-company learning.


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