## 2 Related Work  
Generally speaking, OSS is dynamically developed multi-release software. It will release new functional software as development requirements change. Considering the characteristics of OSS's dynamic development, we can establish two types of OSS RMs to evaluate software reliability. One is to assume that the faults in a continuously released OSS are irrelevant and establish a single-release OSS RM. The second is to assume that there are correlations between faults in successive releases of OSS, so as to establish the corresponding multi-release OSS RM. The single-release OSS RM can evaluate the reliability of each version of an OSS without considering the fault correlation in each version. For example, Lin and Lin Lin and Lin (2018) proposed an OSS RM based on a rate queue theory. To solve the problem of delay in FD and the elimination of OSS, considering that the FD process of OSS obeys a Pareto distribution, Huang et al. Huang et al. (2018) proposed an RM of OSS considering the bounded generalized Pareto distribution. By studying the early FD process for OSS, Lee Lee (2018) proposed a method to predict the reliability of OSS. Through a systematic study of the OSS development process, Yamada and Tamura [16] proposed a reliability measurement and evaluation methods of OSS. Through extensive research on OSS projects, Miko et al. [17] have pointed out that traditional CSS RMs can be used for the reliability assessment of OSS projects. But no software RM can be applied to all OSS development and testing environments. Considering the remnant faults from the previous version and faults generated by changes in software functions, features, and components in the newly released version of an OSS, Zhu and Pham [18] established the RM of OSS. Wang [19; 20] proposed two OSS RMs. One considered that the introduction of faults followed a Pareto distribution, and the other considered that FD and fault introduction changed nonlinearly over time. Huang et al. [21] proposed an ID software RM that considers the decreasing fault fluctuation rate function. Considering the factors of learning and tester fatigue during software testing, Yaghoobi and Leung [22] proposed a software RM based on learning and fatigue factors. Considering the different issue types in the issue tracking system, Singh et al. [23] proposed two different software RMs to evaluate the reliability of OSS. Singhal et al. [24] proposed two software RMs that consider stochastic debugging for OSS. Considering the importance of test coverage in software testing, Singhal et al. [24] proposed two software reliability models: a perfect debugging model based on fault removal efficiency, and an imperfect debugging model based on operating environment uncertainty. Liu and Xie [25] proposed a new software RM based on a gray system theory and validated the effectiveness of the PM through experiments. Jagtap [26] proposed a hybrid nonparametric method to predict software faults and evaluate software reliability. In addition, Garg et al. [27] proposed a hybrid method to select the optimal software RM, as no software RM can demonstrate optimal reliability evaluation performance in all software testing environments. Singh et al. [28] presented a method to select the optimal software RMs based on integrating CRITIC and CODAS. Considering the significant correlation between software RMs and fault data sets, the optimal software RM should be selected for the current software reliability evaluation. Yaghoobi [29] proposed two methods to select the current optimal software RM. On the other hand, a multi-release OSS RM can assess the reliability of each version of an OSS considering the fault correlation in each version. For example, Yang et al. [30] proposed RMs of OSS that consider delays between FD and fault removal. Saraf et al. [31] proposed a general multi-release software framework with FD and correction considering the ID and changing point. Khurshid et al. [32] also proposed a multi-release OSS RM considering the changing point and ID.