一句话：提出 VDet for Java，一个基于 Transformer 的 VS Code 插件，用于检测 Java 代码中的多类漏洞。
自动漏洞检测对提升应用安全性至关重要，并应尽早介入软件开发生命周期以降低风险。尽管深度学习在软件漏洞检测中取得进展，开发环境尚未充分利用其能力。本文把 Transformer 架构集成到一个开发者友好的代码安全工具中，提出 VDet for Java：一个基于 Transformer 的 VS Code 扩展，可在 Java 文件中发现漏洞。初步模型评估显示，该工具在多标签分类上达到 98.9% 的准确率，并能检测多达 21 种漏洞类型。
Automatic vulnerability detection is of paramount importance to promote the security of an application and should be exercised at the earliest stages within the software development life cycle (SDLC) to reduce the risk of exposure. Despite the advancements with state-of-the-art deep learning techniques in software vulnerability detection, the development environments are not yet leveraging their performance. In this work, we integrate the Transformers architecture, one of the main highlights of advances in deep learning for Natural Language Processing, within a developer-friendly tool for code security. We introduce VDet for Java, a transformer-based VS Code extension that enables one to discover vulnerabilities in Java files. Our preliminary model evaluation presents an accuracy of 98.9% for multi-label classification and can detect up to 21 vulnerability types. The demonstration of our tool can be found at https://youtu.be/OjiUBQ6TdqE, and source code and datasets are available at https://github.com/TQRG/VDET-for-Java.