Software reliability models are one of the most generally used mathematical tool for estimation of reliability, failure rate and number of remaining faults in the software. Existing software reliability models are designed to follow waterfall software development life cycle process. These existing models do not take advantage of iterative software development process. In this paper, a new failure rate model centered on iterative software development life cycle process has been developed. It aims to integrate a new modulation factor for incorporating varying needs in each phase of iterative software development process. It comprises imperfect debugging with the possibility of fault introduction and removal of multiple faults in an interval as iterative development of the software proceeds. The proposed model has been validated on twelve iterations of Eclipse software failure dataset and nine iterations of Java Development toolkit (JDT) software failure dataset. Parameter estimation for the proposed model has been done by hybrid particle swarm optimization and gravitational search algorithm. Experimental results in-terms of goodness-of-fit shows that proposed model has outperformed Jelinski Moranda, Shick Wolverton, Goel Okummotto Imperfect debugging, GS Mahapatra, Modified Shick Wolverton in 83.33% of iterations for eclipse dataset and 77.77% of iterations for JDT dataset.

软件可靠性模型是估计软件可靠性、故障率和剩余故障数量的最常用的数学工具之一。现有的软件可靠性模型旨在遵循瀑布式软件开发生命周期过程。这些现有模型没有利用迭代软件开发过程。在本文中，开发了一种以迭代软件开发生命周期过程为中心的新故障率模型。它旨在集成一个新的调制因子，以便在迭代软件开发过程的每个阶段结合不同的需求。它包括不完美的调试，随着软件的迭代开发的进行，可能会在一个时间间隔内引入和消除多个故障。所提出的模型已在 Eclipse 软件故障数据集的 12 次迭代和 Java 开发工具包 (JDT) 软件故障数据集的 9 次迭代上得到验证。所提出模型的参数估计是通过混合粒子群优化和引力搜索算法完成的。拟合优度方面的实验结果表明，所提出的模型在 eclipse 数据集的 83.33% 的迭代和 JDT 数据集的 77.77% 的迭代中优于 Jelinski Moranda、Shick Wolverton、Goel Okummotto Imperfect debugging、GS Mahapatra、Modified Shick Wolverton .