Attribute Selection and Imbalanced Data: Problems in Software Defect Prediction

Abstract

The data mining and machine learning community is often faced with two key problems: working with imbalanced data and selecting the best features for machine learning. This paper presents a process involving a feature selection technique for selecting the important attributes and a data sampling technique for addressing class imbalance. The application domain of this study is software engineering, more specifically, software quality prediction using classification models. When using feature selection and data sampling together, different scenarios should be considered. The four possible scenarios are: (1) feature selection based on original data, and modeling (defect prediction) based on original data; (2) feature selection based on original data, and modeling based on sampled data; (3) feature selection based on sampled data, and modeling based on original data; and (4) feature selection based on sampled data, and modeling based on sampled data. The research objective is to compare the software defect prediction performances of models based on the four scenarios. The case study consists of nine software measurement data sets obtained from the PROMISE software project repository. Empirical results suggest that feature selection based on sampled data performs significantly better than feature selection based on original data, and that defect prediction models perform similarly regardless of whether the training data was formed using sampled or original data.