Abstract
AbstractAlthough there is extensive literature in software defect prediction techniques, machine learning approaches have yet to be fully explored and in particular, Subgroup Discovery (SD) techniques. SD algorithms aim to find subgroups of data that are statistically different given a property of interest [1,2]. SD lies between predictive (finding rules given historical data and a property of interest) and descriptive tasks (discovering interesting patterns in data). An important difference with classification tasks is that the SD algorithms only focus on finding subgroups (e.g., inducing rules) for the property of interest and do not necessarily describe all instances in the dataset.
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