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首页> 外文期刊>Journal of Computers >Incorporating Burr Type XII Testing-efforts into Software Reliability Growth Modeling and Actual Data Analysis with Applications
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Incorporating Burr Type XII Testing-efforts into Software Reliability Growth Modeling and Actual Data Analysis with Applications

机译：将Burr XII类型的测试工作纳入软件可靠性增长建模和实际数据分析及其应用程序中

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摘要

Software reliability is the probability that the given software functions correctly under a given environment, during the specified period of time. During the software-testing phase, software reliability is highly related to the amount of development resources spent on detecting and correcting latent software errors, i.e. the amount of testing effort expenditures. This paper develops software reliability growth models (SRGM) based on non homogeneous Poisson process (NHPP) which incorporates the Burr Type XII testing-effort functions (TEF). Numerous testing-effort functions for modeling software reliability growth based on NHPP have been proposed in the past decade. This paper shows that the Burr Type XII testingeffort function can be expressed as the actual testing-effort consumption during software development process. Its fault-prediction capability is evaluated through the numerical experiments. SRGM parameters are estimated by least square estimation (LSE) and maximum likelihood estimation (MLE) methods and computational experiments performed on actual software failure data set from various software projects. The results show that the proposed testing-efforts functions predicts fault better than the other existing models. Thus, the proposed models evaluate software reliability more realistically. In addition, the optimal release policy based on reliability and cost criteria for software system are proposed.
机译：软件可靠性是指给定软件在指定时间段内在给定环境下正常运行的概率。在软件测试阶段，软件可靠性与用于检测和纠正潜在软件错误的开发资源量（即测试工作量支出）高度相关。本文基于非均质泊松过程（NHPP）开发了软件可靠性增长模型（SRGM），该模型结合了Burr Type XII测试工作量函数（TEF）。在过去的十年中，已经提出了许多用于基于NHPP建模软件可靠性增长的测试工作功能。本文表明，Burr Type XII测试工作量功能可以表示为软件开发过程中实际的测试工作量消耗。通过数值实验评估了其故障预测能力。 SRGM参数通过最小二乘估计（LSE）和最大似然估计（MLE）方法以及对来自各种软件项目的实际软件故障数据集执行的计算实验来估计。结果表明，所提出的测试工作量功能比其他现有模型能够更好地预测故障。因此，提出的模型可以更实际地评估软件的可靠性。此外，提出了基于可靠性和成本准则的软件系统最优发布策略。

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