The challenge is to integrate these distributions in a reasonable and effective manner and obtain a single probability distribution by combining all prior information and knowledge. [PERSON] [6] highlights the problem that \"If the authors have multiple small data sets that are each individually insufficient to answer the question of interest, then combining them and incorporating engineering or scientific understanding of the process should allow us to extract more from that collection of data.\" However, the models need to be carefully considered and evaluated to ensure that they accurately reflect the data and the underlying physical processes. The models have to be simple enough that they can be distinguished by the data, but at the same time complex enough to capture the physical processes. [PERSON] et al. [7] studied an averaging method that combines the opinions of different experts. However, it does not adequately explain the deviations of different opinions and require a more complex model such as Bayesian model. In [8], the Bayesian approach is introduced to reliability using several examples, but not suitable for the web service composition. Considering both system and subsystem level data, other methods were proposed, such as the average multiple priors [9], pooling method [10], and integrating the derived prior through the system structure [11]. In [12] an integration-based method is proposed to estimate and pool the weighted priors; it utilizes Bayes' theorem to integrate heterogeneous priors. In addition, in the service composition concept, where the nodes are traversed in a given order to provide a service, some related techniques have been applied to solve the raised reliability issues, (e.g., Markov chain [13], Stochastic Reward Networks [14], Game theory [15], and Universal Generating Functions [16]). In the case of a complex system comprising a large number of subsystems and components in the field of reliability engineering, prior knowledge of system reliability can provide information about the subsystem and system levels. In the case of web service composition, system-level module reliability is dependent on subsystem module reliability performance. Therefore, utilizing the full range of existing reliability prior information and testing data at subsystem levels and integrating such multilevel prior information during testing is a challenging task. The calculation of hyperparameters in priors is another aspect that needs to be addressed. If there are no reasonable grounds to specify the hyperparameters of the priors, the advantage of Bayesian analysis can be compromised due to the introduction of additional uncertainty. Unfortunately, the assumption of arbitrary values for the hyperparameters of Bayesian priors is not uncommon [17]. Other existing studies on hyperparameters, such as the invariance principle [18], maximum entropy [19], and Jeffrey principle [20], concern pure heuristic methods, which require large amounts of data and are difficult to confirm, thereby making them unsuitable for practical application [21]. The evaluation should be based on the complete range of prior information, and ignoring this issue may result in costly and inaccurate reliability testing. In this paper, the authors propose a reliability assessment method for web service compositions based on Bayes theory by fully utilizing prior information of web services. The authors propose the method considering two stages of web service compositions--reliability growth stage and reliability demonstration stage--building the reliability test frame for web service composition. Based on the reliabilities of various web services, the authors constructed a Markov model to calculate the reliability of the target web service composition. The model both reflects the different in reliability growth and shows the feature of web service composition.