Locality-based security bug report identification via active learning

Abstract

Security bug report (SBR) identification is a crucial way to eliminate security-critical vulnerabilities during software development. In recent years, many approaches have utilized supervised machine learning (SML) techniques in the SBR identification. However, such approaches often require a large number of labelled bug reports, which are often hard to obtain in practice. Active learning is a potential approach to reducing the manual labelling cost while maintaining good performance. Nevertheless, the existing active learning-based SBR identification approach still yields poor performance due to ignoring the locality in bug reports. To address the above problems, we propose locality-based SBR identification via active learning. Our approach recommends a small part of instances based on locality in bug reports, asks for their labels, and learns the SBR classifier . Specifically, our approach relies on the locality to construct the initial training set, which is designed to address how to start during active learning. Moreover, our approach applies the locality into the query process, which is designed to improve which instance should be queried next during active learning. We conduct experiments on large-scale bug reports (nearly 125K) from six real-world projects. In comparison with three state-of-the-art SML-based and active learning-based SBR identification approaches, our approach can obtain the maximum values of F-Measure (0.8176) and AUC (0.8631). Moreover, our approach requires 16.60% to 71.40% of all bug reports when achieving the optimal performance in these six projects, which improves three approaches from 9.82% to 64.19% on average. As shown from the experimental results, our approach can be more effective and efficient to identify SBRs than the existing approaches.