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首页> 外文期刊>The Journal of Systems and Software >An imperfect software debugging model considering log-logistic distribution fault content function
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An imperfect software debugging model considering log-logistic distribution fault content function

机译：考虑对数逻辑分布故障内容功能的不完善软件调试模型

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摘要

Numerous software reliability growth models based on the non-homogeneous Poisson process assume perfect debugging. Such models, including the Goel-Okumoto, delayed S-shaped, and inflection S-shaped models, have been successfully validated in software testing. However, complex and uncertain test factors, such as test resource, tester skill, or test tool, can seriously affect the testing process. When detected faults are removed, new faults can be introduced in practical testing. The process is referred to as imperfect debugging. Imperfect software debugging models proposed in the literature generally assume a constantly or monotonically decreasing fault introduction rate per fault. These models cannot adequately describe the fault introduction process in a practical test. In this study, we propose an imperfect software debugging model that considers a log-logistic distribution fault content function, which can capture the increasing and decreasing characteristics of the fault introduction rate per fault. We also use several historical fault data sets to validate the performance of the proposed model. The model can suitably fit historical fault data and accurately predict failure behavior. Confidence interval and sensitivity analyses are also conducted.
机译：基于非均匀泊松过程的许多软件可靠性增长模型都可以完美地进行调试。这样的模型，包括Goel-Okumoto，延迟S形和拐弯S形模型，已在软件测试中成功验证。但是，复杂而不确定的测试因素（例如测试资源，测试人员的技能或测试工具）会严重影响测试过程。消除检测到的故障后，可以在实际测试中引入新的故障。该过程称为不完善的调试。文献中提出的不完善的软件调试模型通常假定每个故障的故障引入率持续或单调降低。这些模型无法在实际测试中充分描述故障引入过程。在这项研究中，我们提出了一个不完善的软件调试模型，该模型考虑了对数逻辑分布故障内容功能，可以捕获每个故障的故障引入率的递增和递减特征。我们还使用几个历史故障数据集来验证所提出模型的性能。该模型可以适当地拟合历史故障数据并准确预测故障行为。还进行了置信区间和敏感性分析。

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