Low-code vulnerability identification based on TextCNN

Vulnerability identification is a crucial quality assurance step in software engineering, dedicated to discovering and handling potential errors and abnormal behavior in source code. Most vulnerability detection methods are designed for conventional programming languages. With the widespread adoption of low-code development, there is a need for a vulnerability detection method specifically tailored to low-code environments. Thus, we present a robust low-code vulnerability identification model by integrating Convolutional Neural Network Text Classification (TextCNN) and an attention mechanism. The resulting model is capable of recognizing potential irregular patterns in the low code, assisting developers in promptly identifying and addressing potential software defects. It holds significant importance in enhancing the maintainability, stability, and security of the system. Simultaneously, it offers substantial support for the company's software development efforts and mitigates the risk of software defects. The experimental results demonstrate that the method in this paper can achieve accurate low-code vulnerability identification.