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To prioritize software quality assurance efforts, faultprediction models have been proposed to distinguish faulty modules from clean modules. The performances of such models are often biased due to the skewness or class imbalance of the datasets considered. To improve the prediction performance of these models, sampling techniques have been employed to rebalance the distribution of fault-prone and non-fault-prone modules. The effect of these techniques have been evaluated in terms of accuracy/geometric mean/F1-measure in previous studies, however, these measures do not consider the effort needed to fixfaults. To empirically investigate the effect of sampling techniqueson the performance of software fault prediction models in a morerealistic setting, this study employs Norm(Popt), an effort-awaremeasure that considers the testing effort. We performed two setsof experiments aimed at (1) assessing the …