Abstract Traditional software reliability growth models enable quantitative assessment of the software testing process by characterizing the fault detection in terms of testing time or effort. However, the majority of these models do not identify specific testing activities underlying fault discovery and thus can only provide limited guidance on how to incrementally allocate effort. Although there are several novel studies focused on covariate software reliability growth models, they are limited to model development, application, and assessment. This paper presents a non-homogeneous Poisson process software reliability growth model incorporating covariates based on the discrete Cox proportional hazards model. An efficient and stable expectation conditional maximization algorithm is applied to identify the model parameters. An optimal test activity allocation problem is formulated to maximize fault discovery. The proposed method is illustrated through numerical examples on two data sets.

中文翻译：

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协变量软件可靠性和安全模型的最佳测试活动分配

摘要 传统的软件可靠性增长模型通过根据测试时间或工作量来表征故障检测，从而对软件测试过程进行定量评估。然而，这些模型中的大多数没有识别故障发现背后的特定测试活动，因此只能为如何逐步分配工作量提供有限的指导。尽管有几项新的研究侧重于协变量软件可靠性增长模型，但它们仅限于模型开发、应用和评估。本文提出了一种基于离散 Cox 比例风险模型的包含协变量的非齐次 Poisson 过程软件可靠性增长模型。应用高效稳定的期望条件最大化算法来识别模型参数。优化测试活动分配问题被公式化以最大化故障发现。通过在两个数据集上的数值例子来说明所提出的方法。