Software quality has become a key aspect of any electronic system. In this respect, software reliability is an important quality characteristic and there are many models that aim to estimate the reliability from different perspectives. However, there are no industry established reliability models. There is need to estimate which reliability model has the best performance. In this study several reliability models are analyzed by a soft computing approach, called adaptive neuro-fuzzy inference system (neuro-fuzzy), in order to estimate the models’ capability based on root mean square errors (RMSE). Various aspects of accuracy of some of the well-known software reliability models have been used in this work. According to the results Non-Homogeneous Poisson Process Model (NHPP) is the best software reliability model. A combination of Linear Littlewood-Verall (LV) and NHPP is the optimal combination of two software reliability models. In other words, the best results could be achieved if one combines the LV and NHPP models.

软件质量已成为任何电子系统的一个关键方面。在这方面，软件可靠性是一个重要的质量特性，有许多模型旨在从不同的角度估计可靠性。但是，没有行业建立的可靠性模型。需要估计哪个可靠性模型具有最佳性能。在本研究中，通过称为自适应神经模糊推理系统 (neuro-fuzzy) 的软计算方法分析了几个可靠性模型，以便基于均方根误差 (RMSE) 估计模型的能力。在这项工作中已经使用了一些众所周知的软件可靠性模型的准确性的各个方面。根据结果​​，非齐次泊松过程模型（NHPP）是最好的软件可靠性模型。Linear Littlewood-Verall (LV) 和 NHPP 的组合是两种软件可靠性模型的最佳组合。换句话说，如果结合 LV 和 NHPP 模型，可以获得最好的结果。