###### Abstract
(2019, 2020) studied NHPP SRMs with various fault detection rates to consider the uncertainty of operating environments. [PERSON] et al. [PERSON] et al. (2020) proposed an SRM using a Weibull failure detection rate function while considering the testing time with syntax errors. Moreover, the last five models all assume the generalized probability distribution environmental factor and a constant expected number of faults existing in the software before testing. [PERSON] and [PERSON] [PERSON] and [PERSON] (2020) proposed an NHPP SRM based on the pioneering concepts of software fault dependency and imperfect fault removal. [PERSON] and [PERSON] [PERSON] and [PERSON] (2019) presented a unified approach for modeling software reliability growth while considering imperfect debugging and coverage factors. [PERSON] et al. [PERSON] et al. (2020) proposed a model assuming  
Figure 1: Classification of SRMs. a constant fault reduction factor and the existence of Weibull and exponentiated Weibull distribution functions in the test range. [PERSON] et al. [9] proposed a new software reliability growth model (SRGM) based on the structure of the logit growth model. [PERSON] et al. [10] extended the SRGM to consider incomplete debugging, types of errors, and change points during the testing period. [PERSON] and [PERSON] [11] focused on the parameter estimation algorithm of NHPP-based SRGMs and proposed an expectation-maximization algorithm for generalized defect count data. [PERSON] and [PERSON] [12] proposed a gray NHPP SRM in discrete form and proposed a gray-based software reliability estimation approach for a nonlinear multiobjective programming problem. [PERSON] et al. [13] proposed an integrated SRM framework based on a non-homogeneous Markov process for software reliability prediction. Software reliability growth and the tradeoff between software cost and optimal release depend on the accuracy of an SRM. Thus, in software reliability engineering, building an accurate SRM and effectively balancing software cost, reliability, and optimal release time (ORTime) are important for realizing projected objectives [14]. Prior to release, extensive software testing is performed to ensure safety, stability, and reliability. However, this testing process is time-consuming and expensive. Although shortening the software testing time is more conducive than reducing the cost, it introduces unreliability and increases maintenance costs. Lengthening the software testing time increases costs, and the consequent delay in software release reduces the market share of the product [15]. Therefore, determining the testing time prior to software release is important for minimizing the total software system cost while ensuring an acceptable reliability level and low risk. The software operating environment is where users run application software, and many software companies are shortening their traditional 18-month release cycles. For example, Google Chrome and Mozilla Firefox release new versions every six weeks. Shorter release cycles reduce the number of users waiting for a new release and offer better marketing opportunities to companies. However, the improvement in software product quality with shorter release cycles is unclear because the testing period is also reduced. Many studies have been conducted on cost models and optimal release policies. These studies focus primarily on several areas, including SRMs with perfect or imperfect debugging, testing, and operational cost structures. [PERSON] et al. [16] empirically explored the development process of Mozilla Firefox with a shortened release cycle to compare the impulse rate, average uptime, and number of postrelease bugs in the version with the short release cycle using the existing release cycle. [PERSON] [17] proposed a cost model considering imperfect debugging and a random life cycle, using a penalty cost to determine the optimal release policy for a software system.