Table 5 Comparative analysis with existing vulnerability detector.

From: Vulnerability detection in Java source code using a quantum convolutional neural network with self-attentive pooling, deep sequence, and graph-based hybrid feature extraction

 

Accuracy

F1 Score

Precision

Recall

VulDeepecker121

0.95

0.93

0.92

0.94

SQVDT122

0.97

0.95

0.94

0.96

Exp-Gen123

–

–

0.95

–

PreNNsem124

0.96

0.97

0.96

0.98

ISVSF125

0.95

0.90

–

–

MFXSS126

0.98

0.98

0.97

0.96

VULDEFF127

–

0.88

0.91

0.85

SedSVD128

0.91

0.95

–

–

VulANalyZeR129

0.89

0.90

0.85

0.95

FUNDED130

0.92

–

–

0.94

GraphSPD131

0.80

–

–

–

BiTCN_DRSN132

0.95

0.95

0.92

0.98

VERI133

0.92

0.93

0.94

0.91

Proposed model

0.99

0.97

0.98

0.96

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