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首页> 外文期刊>IEEE Transactions on Reliability >Evaluation and Application of Bounded Generalized Pareto Analysis to Fault Distributions in Open Source Software
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Evaluation and Application of Bounded Generalized Pareto Analysis to Fault Distributions in Open Source Software

机译：开源软件中有界广义帕累托分析在故障分布中的评估与应用

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摘要

In general, one of the most important aspects of software development and project management is how to make predictions and assessments of quality and reliability for developed products. Project data usually will be systematically collected and analyzed during the process of software development. Practically, it would be helpful if developers could identify the most error-prone modules early so that they can optimize testing-resource allocation and increase fault detection effectiveness accordingly. In the past, many research studies revealed the applicability of the Pareto principle to software systems, and some of them reported that the Pareto distribution (PD) model can be used to predict the fault distribution of software. In this paper, a special form of the Generalized PD model, named the Bounded Generalized Pareto distribution (BGPD) model, is further proposed to investigate the fault distributions of Open Source Software (OSS). It can be seen that the BGPD model eliminates the issue which occurred in the classical PD model. Three methods of parameter estimation will be presented, and related experiments are performed based on real OSS failure data. Experimental results show that the BGPD model presents high fitness to the actual failure data of OSS. Finally, the possibility of using early limited fault data to predict the later software fault distribution is also studied. Numerical results indicate that the BGPD model can be trusted to consistently produce accurate estimates of fault predictions during the early stages of development. The findings can provide an effective foundation for managing the necessary activities of software development and testing.
机译：通常，软件开发和项目管理的最重要方面之一是如何对开发产品的质量和可靠性进行预测和评估。在软件开发过程中，通常会系统地收集和分析项目数据。实际上，如果开发人员可以及早发现最容易出错的模块，以便他们可以优化测试资源的分配并相应地提高故障检测的效率，那将是有帮助的。过去，许多研究揭示了帕累托原理在软件系统中的适用性，其中一些报告称帕累托分布（PD）模型可用于预测软件的故障分布。本文进一步提出了一种特殊形式的广义PD模型，即有界广义Pareto分布（BGPD）模型，以研究开源软件（OSS）的故障分布。可以看出，BGPD模型消除了经典PD模型中出现的问题。提出了三种参数估计方法，并基于真实的OSS故障数据进行了相关实验。实验结果表明，BGPD模型对OSS的实际故障数据具有较高的适应性。最后，还研究了使用早期有限故障数据来预测后期软件故障分布的可能性。数值结果表明，在开发的早期阶段，可以相信BGPD模型能够始终如一地产生故障预测的准确估计值。这些发现可以为管理软件开发和测试的必要活动提供有效的基础。

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