Single release data sets.

Software reliability growth models (SRGMs) are universally admitted and employed for reliability assessment. The process of software reliability analysis is separated into two components. The first component is model construction, and the second is parameter estimation. This study concentrates on the second segment parameter estimation. The past few decades of literature observance say that the parameter estimation was typically done by either maximum likelihood estimation (MLE) or least squares estimation (LSE). Increasing attention has been noted in stochastic optimization methods in the previous couple of decades. There are various limitations in the traditional optimization criteria; to overcome these obstacles metaheuristic optimization algorithms are used. Therefore, it requires a method of search space and local optima avoidance. To analyze the applicability of various developed meta-heuristic algorithms in SRGMs parameter estimation. The proposed approach compares the meta-heuristic methods for parameter estimation by various criteria. For parameter estimation, this study uses four meta-heuristics algorithms: Grey-Wolf Optimizer (GWO), Regenerative Genetic Algorithm (RGA), Sine-Cosine Algorithm (SCA), and Gravitational Search Algorithm (GSA). Four popular SRGMs did the comparative analysis of the parameter estimation power of these four algorithms on three actual-failure datasets. The estimated value of parameters through meta-heuristic algorithms are approximately near the LSE method values. The results show that RGA and GWO are better on a variety of real-world failure data, and they have excellent parameter estimation potential. Based on the convergence and R2 distribution criteria, this study suggests that RGA and GWO are more appropriate for the parameter estimation of SRGMs. RGA could locate the optimal solution more correctly and faster than GWO and other optimization techniques.

软件可靠性增长模型（Software Reliability Growth Models，SRGMs）被广泛认可并应用于可靠性评估工作。软件可靠性分析流程可划分为两个核心环节：其一为模型构建，其二为参数估计。本研究聚焦于第二环节——参数估计。过往数十年的文献调研结果表明，参数估计通常采用极大似然估计（Maximum Likelihood Estimation，MLE）或最小二乘估计（Least Squares Estimation，LSE）。近几十年来，随机优化方法受到了越来越多的关注。传统优化准则存在诸多局限，为此研究者们采用元启发式优化算法以突破这些障碍，因此需要具备搜索空间探索与局部最优规避能力的方法。为分析各类已提出的元启发式算法在SRGMs参数估计中的适用性，本研究提出的方案依托多项评价准则，对各类元启发式参数估计方法展开对比分析。在参数估计环节中，本研究采用了四种元启发式算法：灰狼优化器（Grey-Wolf Optimizer，GWO）、再生遗传算法（Regenerative Genetic Algorithm，RGA）、正弦余弦算法（Sine-Cosine Algorithm，SCA）以及引力搜索算法（Gravitational Search Algorithm，GSA）。本研究选取四款主流SRGMs，基于三个真实故障数据集，对上述四种算法的参数估计性能展开对比分析。元启发式算法得到的参数估计值与LSE方法的估计值大致相近。实验结果表明，RGA与GWO在各类真实故障数据上表现更优，具备出色的参数估计潜力。基于收敛性与R²分布准则，本研究认为RGA与GWO更适用于SRGMs的参数估计工作。相较于GWO与其他优化技术，RGA能够更准确、更快速地定位最优解。