)1 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; [PERSON]IL_ADDRESS] (Y.S. ); [EMAIL_ADDRESS] (D.J. )1 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; [PERSON]IL_ADDRESS] (Y.S. ); [EMAIL_ADDRESS] (D.J. )1 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; [PERSON]IL_ADDRESS] (Y.S. ); [EMAIL_ADDRESS] (D.J. )1 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; [PERSON]DRESS] (Y.S. ); [EMAIL_ADDRESS] (D.J. )1 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; [PERSON]IL_ADDRESS]u.cn (Y.S. ); [EMAIL_ADDRESS] (D.J. )1 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; [PERSON]IL_ADDRESS]u.cn (Y.S. ); [EMAIL_ADDRESS] (D.J. )1 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; [PERSON]IL_ADDRESS]u.cn (Y.S. ); [EMAIL_ADDRESS] (Y.S. )1 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; [PERSON]DRESS] (Y.S. ); [EMAIL_ADDRESS] (D.J. )1 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; [PERSON]IL_ADDRESS]u.cn (Y.S. ); [EMAIL_ADDRESS] (Y.S. )1 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; [PERSON]_[PERSON]@bupt. growth of the software to ensure high reliability software demonstration testing [3]. In this context, Bayes reliability inference has been extensively applied to reliability estimation, and a wealth of experts and prior knowledge about system/subsystem performances are associated with it [1]. The Bayesian approach has been adopted because it combines subjective judgment or prior experience with data obtained from test samples. In other words, it uses a combination of existing experience and new test data to assess reliability metrics. In this approach, a posterior distribution is derived based on a prior distribution and a likelihood function, and reliability testing is conducted based on this derived distribution. Following the addition of new sample data, this posterior distribution is employed as a prior distribution during the process of constructing a new posterior distribution. Under the Bayesian framework, historical data are usually used for the analysis of prior distributions with certain parameters [4]. However, the origin of the required prior distributions remains a major concern in Bayesian reliability analysis [5]. The appropriate choice of priors is critical to capture useful information present in historical data. The fundamental advantage of adopting the Bayesian approach is that it augments the quality of data, thereby reducing the uncertainty involved in decision making. Thus, in this case, the prior information is derived from historic data or experts' suggestions. The two types of information regarding unknown parameters can be expressed in the form of statistical distributions, from which further statistical inferences can be made. Then, the posterior distribution is evaluated by integrating the prior distribution and the likelihood function constructed based on the sampling distribution of the data. Bayesian estimation with prior distribution has been extensively studied in the literature, for the reliability assessment, some studies are shown in Table 1. The integration of multiple prior conditions, especially when these prior conditions are derived from various unique information sources, is challenging in Bayesian inference.