## 6 Analysis of Experimental Results
### Detection Performance Comparison  
In order to obtain the data required for the experiment, a crawler script was written to obtain 2000 latest smart contracts and their operation codes from the official website of etherscan.io, including 400 game contracts, 400 social contracts, 400 financial contracts, 400 Gambling contracts, and 400 other contracts that guarantee the richness and randomness of the data. The pre-improved Mythril method and the improved Mythril method were used to conduct experiments on smart contract vulnerabilities, and the artificial statistical results are: Among the 2000 contracts, 236 test contracts have loopholes, and the true detection rate and false alarm rate of the two detection tools are determined, the statistical comparison chart is shown in Figure 6a,b. It can be seen from the figure that among the 2000 contracts, the method before the improvement detected 203 loopholes and 43 false positives; the improved method detected 222 loopholes and 20 false positives, so the false positive rates before and after the improvement are 21% and 9%, respectively. From the experimental results, it can be seen that the accuracy and false positive rate of the improved method have improved. The performance evaluation of smart contract vulnerability detection tools mainly includes the following two aspects. Figure 6: The number of vulnerabilities and false positives detected by Mythril before and after improvement. The real detection rate and false alarm rate of the two detection tools before and after the improvement are shown in **(a,b)**, from which it can be seen that the accuracy rate and false alarm rate of the improved method have been improved. 1. Accuracy: In order to evaluate the pros and cons of detection tools, we first focus on the most common evaluation index, namely accuracy. Accuracy can objectively reflect the most direct detection effect of various detection tools. Usually, the real situation is compared with the matching results of the detection results are used as an important evaluation index, including the following four situations. 1. True positive (true positive, TP): For a certain contract, the detection result of the detection tool has loopholes, and the real situation also has loopholes, that is, the detection result is correct; 2. False positive (false positive, FP): For a certain contract, the detection result of the detection tool is that there is a loophole, but the real situation is no loophole, that is, there is a misjudgment in the detection result; 3. False negative (FN): For a certain contract, the detection result of the detection tool is that there are no loopholes, but the real situation is that there are loopholes, that is, there are missed judgments in the detection results; 4. True negative (true negative, TN): For a certain contract, the detection result of the detection tool is no loopholes, and the real situation is also without loopholes, that is, the detection result is correct. Obviously, the result of TP + FP + FN + TN is 2000 smart contract test samples, and the calculation method of the accuracy rate is shown in Formula (1): \[\text{accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{ FN}}\] (1) 2. F1-Score: F1-Score is an important measurement index in classification problems. It is the harmonic mean of precision and recall. It is often used as the final evaluation standard for some classification problems. Its calculation method is shown in Formulas (2)-(4): \[\text{Precision}=\frac{\text{TP}}{\text{TP}\,+\,\text{FP}}\] (2) \[\text{Recall}=\frac{\text{TP}}{\text{TP}\,+\,\text{FN}}\] (3) \[\text{F1}-\text{Score}=2\times\frac{\text{Precision}\,\times\,\text{ Recall}}{\text{Precision}\,+\,\text{Recall}}\] (4)  
The model proposed in previous studies was selected to compare the accuracy of detection and F1-Score under the same conditions.