## 6 Experimental Data Results and Analysis  
### Experimental Preparation  
Before introducing the experiments, it is essential to present the incentive generation module and the detection module, which are critical components of the experimental framework. These modules play a vital role in generating the necessary data for evaluating the performance of the ACTR algorithm. To assess the significance of the observed differences in performance metrics, we will employ the \(T\)-test for independent samples. This statistical test will provide insights into the reliability of the results, ensuring a rigorous evaluation of the algorithm's effectiveness in reconfiguring the NoC architecture. The detailed discussion is presented as follows. The incentive generation module is designed to generate the states of the entire network-on-chip (NoC) architecture, simulating the operational status of each processor core--specifically, whether damage occurs during operation. This module utilizes a random number generator to randomly assign core states for various fault distributions, with the state information stored in a two-dimensional array. Each core can exist in one of two states: normal or damaged. For example, in a simulated core configuration of size \(4\times 5\) with a column of redundant cores, the module outputs a scenario where four cores are damaged. As shown in Figure 5, 1 indicates that the core is working normally, while 0 indicates that the core is damaged. The generated array provides a comprehensive view of the state of each core in the NoC, serving as the foundation for topology reconfiguration. The initial state of the randomly generated cores in this experiment is a two-dimensional matrix containing 0 s and 1 s, the same as the above example. The detection module is used to extract non-redundant data from a large amount of randomly generated data, serving as a basis for evaluating the success rate of the algorithm with valid data. For example, in a network with a working core size of \(4\times 5\) and one column of redundant cores, there are only 20 unique scenarios when a single core is damaged. If 100,000 random injections are performed, a substantial amount of redundant data may be generated, potentially influencing the results. Consequently, our experiment incorporates a detection module to assess the redundancy among the 100,000 initial states, ensuring that only unique scenarios are considered in the evaluation of the algorithm's performance. The \(T\)-test for independent samples is employed to determine whether the means of two distinct groups are statistically different. This statistical test computes a t-statistic based on the differences between group means, the standard deviations of each group, and the respective sample sizes. A significant t-statistic indicates that the observed mean differences are unlikely to arise by chance, thus providing evidence of the algorithm's effectiveness under various fault conditions. By assessing performance metrics such as successful reconfiguration rate (SRR) and average recovery time (ART), the \(t\)-test evaluates the reliability and potential advantages of the ACTR algorithm in reconfiguring the NoC topology [38; 39]. The methodology for applying the \(T\)-test begins with data collection, wherein performance metrics are recorded across 1000 iterations for each unique initial state generated by the incentive generation module. Each iteration simulates different configurations of the NoC, ensuring a comprehensive dataset. The performance metrics are subsequently organized into groups based on predefined conditions, such as varying numbers of damaged cores. The \(T\)-test is then conducted to compare mean performance metrics between these groups. A significance level, typically set at \(\alpha=0.05\), is used to evaluate whether the observed differences are statistically significant.