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The label quality of defect data sets has a direct influence on thereliability of defect prediction models. In this study, formulti-version-project defect data sets, we propose an approach to automaticallydetecting instances with inconsistent labels (i.e. the phenomena of instanceshaving the same source code but different labels over multiple versions of asoftware project) and understand their influence on the evaluation andinterpretation of defect prediction models. Based on five multi-version-projectdefect data sets (either widely used or the most up-to-date in the literature)collected by diverse approaches, we find that: (1) most versions in theinvestigated defect data sets contain inconsistent labels with varying degrees;(2) the existence of inconsistent labels in a training data set mayconsiderably change the prediction performance of a defect prediction model aswell as can lead to the identification of substantially different truedefective modules; and (3) the importance ranking of independent variables in adefect prediction model can be substantially shifted due to the existence ofinconsistent labels. The above findings reveal that inconsistent labels indefect data sets can profoundly change the prediction ability andinterpretation of a defect prediction model. Therefore, we strongly suggestthat practitioners should detect and exclude inconsistent labels in defect datasets to avoid their potential negative influence on defect prediction models.What is more, it is necessary for researchers to improve existing defect labelcollection approaches to reduce inconsistent labels. Furthermore, there is aneed to re-examine the experimental conclusions of previous studies usingmulti-version-project defect data sets with a high ratio of inconsistentlabels.

An extensive empirical study of inconsistent labels in multi-version-project defect data sets