University of Minnesota
Software Engineering Center
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Ayse Bener

Recent Publications

Implications of Ceiling Effects in Defect Predictors

Context: There are many methods that input static code features and output a predictor for faulty code modules. These data mining methods have hit a "performance ceiling"; i.e., some inherent upper bound on the amount of information offered by, say, static code features when identifying modules which contain faults. Objective: We seek an explanation for this ceiling effect. Perhaps static code features have "limited information content"; i.e. their information can be quickly and completely discovered by even simple learners.