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Software Reliability Prediction and Analysis Using Queueing Models with Multiple Change-Points

机译:软件可靠性预测和分析使用多种变化点的排队模型

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Over the past three decades, many software reliability growth models (SRGMs) were proposed and they are aimed at predicting and estimating software reliability. One common assumption of these conventional SRGMs is that detected faults will be removed immediately. In reality, this assumption may not be reasonable and may not always occur. Developers need time to identify the root causes of detected faults and then fix them. Besides, during debugging the fault correction rate may not he a constant and could be changed at some certain points as time proceeds. Consequently, in this paper, we will explore and study how to apply queueing model to investigate the fault correction process during software development. We propose an extended infinite server queueing model with multiple change-points to predict and assess software reliability. Experimental results based on real failure data show that proposed model can depicts the change of fault correction rates and predict the behavior of software development more accurately than traditional SRGMs.
机译:在过去的三十年中,提出了许多软件可靠性增长模型(SRGMS),旨在预测和估算软件可靠性。这些传统SRGM的一个常见假设是检测到的故障将立即移除。实际上,这种假设可能不合理,并且可能并不总是发生。开发人员需要时间来确定检测到的故障的根本原因,然后修复它们。此外,在调试过程中,故障校正率可能不是常数,并且随着时间的继续,可以在某些点处改变。因此,在本文中,我们将探索和研究如何应用排队模型来研究软件开发过程中的故障校正过程。我们提出了一个扩展的无限服务器排队模型,具有多个变化点来预测和评估软件可靠性。基于实际故障数据的实验结果表明,所提出的模型可以描绘故障校正速率的变化,并比传统的SRGM更准确地预测软件开发的行为。

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