SCALE: Constructing Structured Natural Language Comment Trees for Software Vulnerability Detection
<|reference_start|>SCALE: Constructing Structured Natural Language Comment Trees for Software Vulnerability Detection: Recently, there has been a growing interest in automatic software vulnerability detection. Pre-trained model-based approaches have demonstrated superior performance than other Deep Learning (DL)-based approaches in detecting vulnerabilities. However, the existing pre-trained model-based approaches generally employ code sequences as input during prediction, and may ignore vulnerability-related structural information, as reflected in the following two aspects. First, they tend to fail to infer the semantics of the code statements with complex logic such as those containing multiple operators and pointers. Second, they are hard to comprehend various code execution sequences, which is essential for precise vulnerability detection.   To mitigate the challenges, we propose a Structured Natural Language Comment tree-based vulnerAbiLity dEtection framework based on the pre-trained models, named SCALE. The proposed Structured Natural Language Comment Tree (SCT) integrates the semantics of code statements with code execution sequences based on the Abstract Syntax Trees (ASTs). Specifically, SCALE comprises three main modules: (1) Comment Tree Construction, which aims at enhancing the model's ability to infer the semantics of code statements by first incorporating Large Language Models (LLMs) for comment generation and then adding the comment node to ASTs. (2) Structured Natural Language Comment Tree Construction}, which aims at explicitly involving code execution sequence by combining the code syntax templates with the comment tree. (3) SCT-Enhanced Representation, which finally incorporates the constructed SCTs for well capturing vulnerability patterns.<|reference_end|>
@article{wen2024scale:,
    title={SCALE: Constructing Structured Natural Language Comment Trees for
  Software Vulnerability Detection},
    author={Xin-Cheng Wen, Cuiyun Gao, Shuzheng Gao, Yang Xiao, Michael R. Lyu},
    journal={arXiv preprint arXiv:2403.19096},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19096},
    primaryClass={cs.SE cs.CR}
}