The performance of the learning models will intensely rely on the characteristics of the training data. The previous outcomes recommend that the overlapping between classes and the presence of noise have the most grounded impact on the performance of learning algorithm, and software defect datasets are no exceptions. The class overlap problem is concerned with the performance of machine learning classifiers critical problem is class overlap in which data samples appear as valid examples of more than one class which may be responsible for the presence of noise in datasets. We aim to investigate how the presence of overlapped instances in a dataset influences the classifier’s performance, and how to deal with class overlapping problem. To have a close estimate of class overlapping, we have proposed four different measures namely, nearest enemy ratio, subconcept ratio, likelihood ratio and soft margin ratio. We performed our investigations using 327 binary defect classification datasets obtained from 54 software projects, where we first identified overlapped datasets using three data complexity measures proposed in the literature. We also include treatment effort into the prediction process. Subsequently, we used our proposed measures to find overlapped instances in the identified overlapped datasets. Our results indicated that by training a classifier on a training data free from overlapped instances led to an improved classifier performance on the test data containing overlapped instances. The classifiers perform significantly better when the evaluation measure takes the effort into account. © 2017, Springer-Verlag Wien.