Reliability and maintainability analysis and its toolbased on deep learning for fault big data

Y. Tamura, S. Yamada
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引用次数: 7

Abstract

Recently, many fault data sets are recorded on the bug tracking systems. These bug tracking systems are used on the Website for such as open source project management. The software managers of open source projects can comprehend the fault status by using the bug tracking system. However, these software systems for development support such as the bug tracking system cannot estimate the future trend of fault data. We discuss the methods of reliability and maintainability assessment based on the deep learning for fault big data. In particular, we develop the reliability and maintainability analysis tool based on deep learning for fault big data by using the latest programing technology. Moreover, we show several numerical illustrations of the developed software tool by using the fault big data in the actual software projects.
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基于深度学习的故障大数据可靠性与可维护性分析及其工具
近年来,在缺陷跟踪系统中记录了大量的故障数据集。这些漏洞跟踪系统用于网站上的开源项目管理等。开源项目的软件管理者可以通过bug跟踪系统来了解故障状态。然而,这些用于开发支持的软件系统,如bug跟踪系统,无法估计故障数据的未来趋势。探讨了基于深度学习的故障大数据可靠性和可维护性评估方法。特别是,我们利用最新的编程技术,开发了基于深度学习的故障大数据可靠性和可维护性分析工具。并通过实际软件项目中故障大数据的应用,对所开发的软件工具进行了数值演示。
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