# IFDA: Intermittent Fault Diagnosis Algorithm for Augmented Cubes Under the PMC Model
## 6 Simulation Experiment  
In this section, the performance of the _IFDA_ is evaluated through simulation experiments. Prior research, as evidenced in [22; 23; 24], has demonstrated the efficacy and performance of diagnosis algorithms grounded in network decomposition strategy. Therefore, this experiment simulates the efficacy of the algorithm in diagnosing intermittent fault nodes and evaluates the impact of relevant parameters on the final diagnostic outcomes. In the experiments, we assume that the intermittent fault nodes are randomly distributed. The IFDA is programmed in Python and executed using Python 3.10. To mitigate experimental errors arising from random functions, the mean value derived from 10,000 algorithm runs is considered as the experimental outcome. For the simulation experiment, the evaluation parameters consist of three important metrics: Detection Accuracy, Precision, and Recall (also known as True Positive Rate, TPR). These metrics are utilized to evaluate the final performance of the algorithm. The concept of _Accuracy_/ has the same definition as the standard Accuracy, but it is calculated after the third step of the intermittent fault diagnosis algorithm (_IFDA_) is finished. In a multiprocessor system, the number of test rounds between adjacent nodes has a direct impact on the syndrome. Additionally, the probability of intermittent faulty nodes showing failures, represented by _Ratio_ in this experiment, is another key parameter that affects the syndrome. Therefore, this paper will assess how both the Ratio and the number of test rounds influence the experimental results. The following are the in-depth definitions of the relevant evaluation parameters:  
* Detection Accuracy (Accuracy, DA) [21]: The ratio of the number of nodes that are correctly diagnosed to the total number of nodes. * Recall/TPR [21]: The ratio of the number of faulty nodes that are correctly diagnosed to the total number of faulty nodes. * Precision [21]: The ratio of the number of faulty nodes that are correctly diagnosed to the total number of diagnosed faulty nodes. To begin with, the experiment shown in Table 3 evaluates how the algorithm performs in augmented cubes of different dimensions. At the same time, the data from Table 3 are depicted in Figure 4. From the experimental results of Precision, we can observe that the algorithm does not make false diagnoses by labeling non-faulty nodes as faulty. Moreover, as the dimension of the augmented cube goes up, both the _Accuracy_ and _Accuracy_ parameters show a steady increase. The reason for the increase in Accuracy is that in higher-dimensional networks, each node has more neighboring nodes, which raises the probability of 0-path connections between adjacent nodes. It is important to note that the decrease in the Recall parameter is also due to this same factor. The improvement in Accuracy is a result of the growing difference between the fault bound of the intermittent fault diagnosis algorithm (_IFDA_) and the intermittent fault diagnosability. This growing difference enables more subpaths to meet the Type-I criteria set out in Lemma 15. Furthermore, considering the influence of the number of tests conducted between adjacent nodes on the diagnostic outcomes, the corresponding experimental results are presented in Table 4 and Figure 5a. In this experiment, the dimension of network is set to 10, and the probability of intermittent faulty nodes exhibiting failures is assumed to be 80%. The experimental results reveal that the Precision and Accuracy' parameters remain relatively constant. As the number of rounds increases, the Accuracy parameter gradually improves and eventually reaching 100%. Additionally, the Recall parameter rapidly increases with the rise in the number of rounds, as multiple tests enhance the probability of nodes exhibiting failure states. It can be concluded that increasing the number of tests between adjacent nodes significantly improves the accuracy of the algorithm's diagnostic results.