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Software vulnerabilities pose significant risks to computer systems,impacting our daily lives, productivity, and even our health. Identifying andaddressing security vulnerabilities in a timely manner is crucial to preventhacking and data breaches. Unfortunately, current vulnerability identificationmethods, including classical and deep learning-based approaches, exhibitcritical drawbacks that prevent them from meeting the demands of thecontemporary software industry. To tackle these issues, we present JFinder, anovel architecture for Java vulnerability identification that leverages quadself-attention and pre-training mechanisms to combine structural informationand semantic representations. Experimental results demonstrate that JFinderoutperforms all baseline methods, achieving an accuracy of 0.97 on the CWEdataset and an F1 score of 0.84 on the PROMISE dataset. Furthermore, a casestudy reveals that JFinder can accurately identify four cases ofvulnerabilities after patching.

JFinder: A Novel Architecture for Java Vulnerability Identification Based Quad Self-Attention and Pre-training Mechanism