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Applying security patches in open source software timely is critical forensuring the security of downstream applications. However, it is challenging toapply these patches promptly because notifications of patches are oftenincomplete and delayed. To address this issue, existing approaches employdeep-learning (DL) models to identify additional vulnerability patches bydetermining whether a code commit addresses a vulnerability. Nonetheless, theseapproaches suffer from low accuracy due to the imprecise context provided forthe patches. To provide precise context for patches, we propose amulti-granularity slicing algorithm and an adaptive-expanding algorithm toaccurately identify code related to the patches. Additionally, the precisecontext enables to design an iterative identification framework, CompVPD, whichutilizes the human validation results, and substantially improve theeffectiveness. We empirically compare CompVPD with fourstate-of-the-art/practice (SOTA) approaches in identifying vulnerabilitypatches. The results demonstrate that CompVPD improves the F1 score by 20%compared to the best scores of the SOTA approaches. Additionally, CompVPDcontributes to security practice by helping identify 20 vulnerability patchesand 18 fixes for high-risk bugs from 2,500 recent code commits in five highlypopular open-source projects.

CompVPD: Iteratively Identifying Vulnerability Patches Based on Human Validation Results with a Precise Context