SAIS: Self-Adaptive Identification of Security Bug Reports

Abstract

Among various bug reports (BRs), security bug reports (SBRs) are unique because they require immediate concealment and fixes. When SBRs are not identified in time, attackers can exploit the vulnerabilities. Prior work identifies SBRs via text mining, which requires a predefined keyword list and trains a classifier with known SBRs and non-security bug reports (NSBRs). The former approach is not reliable, because (1) as the contexts of security vulnerabilities and terminology of SBRs change over time, the predefined list will become out-dated; and (2) users may have insufficient SBRs for training. We introduce a semi-supervised learning-based approach, Sais , to adaptively and reliably identify SBRs. Given a project's BRs containing some labeled SBRs, many more NSBRs, and unlabeled BRs, Sais iteratively mines keywords, trains a classifier based on the keywords from the labeled data, classifies unlabeled BRs, and augments its training data with the newly labeled BRs. Our evaluation shows that Sais is useful for identifying SBRs.