When assessing a software-based system, the results of statistical inference on operational testing data can provide strong support for software reliability claims. For inference, this data (i.e. software successes and failures) is often assumed to arise in an independent, identically distributed (i.i.d.) manner. In this paper we show how conservative Bayesian approaches make this assumption unnecessary, by incorporating one's doubts about the assumption into the assessment. We derive conservative confidence bounds on a system's probability of failure on demand (pfd), when operational testing reveals no failures. The generality and utility of the confidence bounds are demonstrated in the assessment of a nuclear power-plant safety-protection system, under varying levels of skepticism about the i.i.d. assumption.

翻译：在评估基于软件的系统时,对运行测试数据的统计推论结果可以为软件可靠性索赔提供有力的支持。据推断,这些数据(即软件的成功和失败)通常以独立、分布相同(即d)的方式出现。在本文中,我们通过将人们对假设的怀疑纳入评估,表明保守的巴伊西亚方法如何使这一假设变得没有必要。当运行测试显示没有失败时,我们从系统按要求(pfd)的失灵概率(pfd)中得出保守的信任界限。在评估核电厂安全保护系统时,在对i.d假设的不同怀疑程度下,可以证明信任界限的普遍性和效用。