Data Quality Matters: A Case Study on Data Label Correctness for Security Bug Report Prediction

In the research of mining software repositories, we need to label a large amount of data to construct a predictive model. The correctness of the labels will affect the performance of a model substantially. However, limited studies have been performed to investigate the impact of mislabeled instances on a predictive model. To bridge the gap, in this article, we perform a case study on the security bug report (SBR) prediction. We found five publicly available datasets for SBR prediction contains many mislabeled instances, which lead to the poor performance of SBR prediction models of recent studies (e.g., the work of Peters et al. and Shu et al. ). Furthermore, it might mislead the research direction of SBR prediction. In this article, we first improve the label correctness of these five datasets by manually analyzing each bug report, and we find 749 SBRs, which are originally mislabeled as Non-SBRs (NSBRs). We then evaluate the impacts of datasets label correctness by comparing the performance of the classification models on both the noisy (i.e., before our correction) and the clean (i.e., after our correction) datasets. The results show that the cleaned datasets result in improvement in the performance of classification models. The performance of the approaches proposed by Peters et al. and Shu et al. on the clean datasets is much better than on the noisy datasets. Furthermore, with the clean datasets, the simple text classification models could significantly outperform the security keywords-matrix-based approaches applied by Peters et al. and Shu et al.