Software reliability growth models (SRGM) enable failure data collected during testing. Specifically, nonhomogeneous Poisson process (NHPP) SRGM are the most commonly employed models. While software reliability growth models are important, efficient modeling of complex software systems increases the complexity of models. Increased model complexity presents a challenge in identifying robust and computationally efficient algorithms to identify model parameters and reduces the generalizability of the models. Existing studies on traditional software reliability growth models suggest that NHPP models characterize defect data as a smooth continuous curve and fail to capture changes in the defect discovery process. Therefore, the model fits well under ideal conditions, but it is not adaptable and will only fit appropriately shaped data. Neural networks and other machine learning methods have been applied to greater effect [5], however limited due to lack of large samples of defect data especially at earlier stages of testing.

翻译：软件可靠性增长模型（SRGM）能够利用测试过程中收集的失效数据。其中，非齐次泊松过程（NHPP）SRGM是最常用的模型。尽管软件可靠性增长模型至关重要，但对复杂软件系统的高效建模增加了模型的复杂度。模型复杂度的提升给识别稳健且计算高效的参数估计算法带来了挑战，并降低了模型的泛化能力。现有关于传统软件可靠性增长模型的研究表明，NHPP模型将缺陷数据描述为平滑连续曲线，无法捕捉缺陷发现过程中的变化。因此，该模型在理想条件下拟合效果良好，但缺乏适应性，仅能拟合形态合适的数据。神经网络及其他机器学习方法虽已取得更优效果[5]，但由于缺乏大样本缺陷数据（尤其是在测试早期阶段）而受限。