The efficiency and performance of a software application largely depend on the testing strategy adopted by the firm. Apart from the tools, techniques, and skills used for testing, the duration also plays an important influence in establishing software reliability. This defines the operational performance of the software. The testing duration decision is dependent on the failure behavior depicted by the software during the process of testing and the cost spent at various phases of development. In this paper, we study the multi-release model whose fault removal process is affected by random irregular fluctuations (white noise) and error generation phenomenon and determine the optimal time of testing for a multi-release-based software. The fault removal process is governed by the phenomenon of testing coverage which is affected by random fluctuations. The model shows encouraging results as it handles the stochastic property of the fault detection process. The optimal testing time is determined with the goal to minimize the expected development cost related to software while achieving the desired reliability levels of software for that release using a Genetic Algorithm. A real-life four-release fault dataset of tandem computers has been used to numerically demonstrate the methodology. It is observed through sensitivity analysis that the presence of white noise directly affects the cost and optimal testing duration. The potential to improve sensitivity, flexibility, early detection, discovering unsuspected patterns, and boost fault diagnosis is enhanced by collecting irregular fluctuations in the fault detection rate. This technology, by going beyond existing methodologies, has distinct advantages for detecting faults and can contribute to more dependable and efficient systems in a variety of domains.

软件应用程序的效率和性能很大程度上取决于公司采用的测试策略。除了测试所使用的工具、技术和技能之外，持续时间对于建立软件可靠性也起着重要的影响。这定义了软件的操作性能。测试持续时间的决定取决于软件在测试过程中描述的故障行为以及开发各个阶段所花费的成本。在本文中，我们研究了多版本模型，其故障排除过程受到随机不规则波动（白噪声）和错误生成现象的影响，并确定了基于多版本的软件的最佳测试时间。故障排除过程受随机波动影响的测试覆盖率现象的控制。该模型显示了令人鼓舞的结果，因为它处理了故障检测过程的随机特性。确定最佳测试时间的目的是最大限度地减少与软件相关的预期开发成本，同时使用遗传算法实现该版本的软件所需的可靠性水平。现实生活中的串联计算机的四次发布故障数据集已用于数值演示该方法。通过敏感性分析发现，白噪声的存在直接影响成本和最佳测试持续时间。通过收集故障检测率的不规则波动，可以增强提高灵敏度、灵活性、早期检测、发现未预料到的模式以及促进故障诊断的潜力。该技术超越了现有的方法，在检测故障方面具有明显的优势，并且可以为各个领域的系统提供更可靠、更高效的贡献。