Multilevel Semantic Embedding of Software Patches: A Fine-to-Coarse Grained Approach Towards Security Patch Detection

TL;DR

This paper introduces MultiSEM, a multilevel semantic embedding model that combines word-level, line-level, and description semantics to improve security patch detection in software, outperforming existing models.

Contribution

The paper presents a novel multilevel semantic embedding approach, MultiSEM, that integrates word, line, and description semantics for enhanced security patch detection.

Findings

Achieved 22.46% improvement on PatchDB in F1 score.

Achieved 9.21% improvement on SPI-DB in F1 score.

Demonstrated robustness and superiority over state-of-the-art models.

Abstract

The growth of open-source software has increased the risk of hidden vulnerabilities that can affect downstream software applications. This concern is further exacerbated by software vendors' practice of silently releasing security patches without explicit warnings or common vulnerability and exposure (CVE) notifications. This lack of transparency leaves users unaware of potential security threats, giving attackers an opportunity to take advantage of these vulnerabilities. In the complex landscape of software patches, grasping the nuanced semantics of a patch is vital for ensuring secure software maintenance. To address this challenge, we introduce a multilevel Semantic Embedder for security patch detection, termed MultiSEM. This model harnesses word-centric vectors at a fine-grained level, emphasizing the significance of individual words, while the coarse-grained layer adopts entire code lines for vector representation, capturing the essence and interrelation of added or removed lines. We further enrich this representation by assimilating patch descriptions to obtain a holistic semantic portrait. This combination of multi-layered embeddings offers a robust representation, balancing word complexity, understanding code-line insights, and patch descriptions. Evaluating MultiSEM for detecting patch security, our results demonstrate its superiority, outperforming state-of-the-art models with promising margins: a 22.46\% improvement on PatchDB and a 9.21\% on SPI-DB in terms of the F1 metric.