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Standard automatic methods for recognizing problematic development commitscan be greatly improved via the incremental application of human+artificialexpertise. In this approach, called EMBLEM, an AI tool first explore thesoftware development process to label commits that are most problematic. Humansthen apply their expertise to check those labels (perhaps resulting in the AIupdating the support vectors within their SVM learner). We recommend thishuman+AI partnership, for several reasons. When a new domain is encountered,EMBLEM can learn better ways to label which comments refer to real problems.Also, in studies with 9 open source software projects, labelling via EMBLEM'sincremental application of human+AI is at least an order of magnitude cheaperthan existing methods ($\approx$ eight times). Further, EMBLEM is veryeffective. For the data sets explored here, EMBLEM better labelling methodssignificantly improved $P_{opt}20$ and G-scores performance in nearly all theprojects studied here.

Better Data Labelling with EMBLEM (and how that Impacts Defect Prediction)