  Software vulnerability detection is generally supported by automated static
analysis tools, which have recently been reinforced by deep learning (DL)
models. However, despite the superior performance of DL-based approaches over
rule-based ones in research, applying DL approaches to software vulnerability
detection in practice remains a challenge due to the complex structure of
source code, the black-box nature of DL, and the domain knowledge required to
understand and validate the black-box results for addressing tasks after
detection. Conventional DL models are trained by specific projects and, hence,
excel in identifying vulnerabilities in these projects but not in others. These
models with poor performance in vulnerability detection would impact the
downstream tasks such as location and repair. More importantly, these models do
not provide explanations for developers to comprehend detection results. In
contrast, Large Language Models (LLMs) have made lots of progress in addressing
these issues by leveraging prompting techniques. Unfortunately, their
performance in identifying vulnerabilities is unsatisfactory. This paper
contributes \textbf{\DLAP}, a \underline{\textbf{D}}eep
\underline{\textbf{L}}earning \underline{\textbf{A}}ugmented LLMs
\underline{\textbf{P}}rompting framework that combines the best of both DL
models and LLMs to achieve exceptional vulnerability detection performance.
Experimental evaluation results confirm that \DLAP outperforms state-of-the-art
prompting frameworks, including role-based prompts, auxiliary information
prompts, chain-of-thought prompts, and in-context learning prompts, as well as
fine-turning on multiple metrics.
