Software, while beneficial, poses potential cybersecurity risks due to
inherent vulnerabilities. Detecting these vulnerabilities is crucial, and deep
learning has shown promise as an effective tool for this task due to its
ability to perform well without extensive feature engineering. However, a
challenge in deploying deep learning for vulnerability detection is the limited
availability of training data. Recent research highlights the deep learning
efficacy in diverse tasks. This success is attributed to instruction
fine-tuning, a technique that remains under-explored in the context of
vulnerability detection. This paper investigates the capability of models,
specifically a recent language model, to generalize beyond the programming
languages used in their training data. It also examines the role of natural
language instructions in enhancing this generalization. Our study evaluates the
model performance on a real-world dataset to predict vulnerable code. We
present key insights and lessons learned, contributing to understanding the
deep learning application in software vulnerability detection.

通过对最新的语言模型的能力进行评估，研究了该模型在其训练数据中所使用的编程语言之外的领域的普适性，并检查了自然语言指令在提高这种普适性中的作用。研究通过对真实数据集的模型性能进行评估，以预测代码的脆弱性，从而对于深度学习在软件脆弱性检测中的应用作出了重要贡献。