Fault Big Data Analysis Tool based on Deep Learning

Yoshinobu Tamura; Shigeru Yamada

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Fault Big Data Analysis Tool based on Deep Learning

机译：基于深度学习的故障大数据分析工具

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Software managers can obtain useful information from many fault data sets recorded on bug tracking systems (BTS). However, it is difficult to find helpful measures for software reliability, maintainability, and performability, because the data collected on the BTS are mixed with qualitative and quantitative ones. This paper discusses the methods of reliability, maintainability, and performability assessment by deep learning for big data in terms of software faults. Specifically, we implement the reliability, maintainability, and performability analysis tool discussed in our method by using the latest programing technology. Moreover, we show several performance examples of the implemented application software by using the fault big data observed in the practical projects.

机译：软件经理可以从错误跟踪系统（BTS）上记录的许多故障数据集获取有用的信息。但是，很难找到软件可靠性，可维护性和可操作性的有用措施，因为BTS上收集的数据与定性和定量的数据混合。本文讨论了在软件故障方面深入了解大数据的可靠性，可维护性和可操作性评估方法。具体而言，我们通过使用最新的编程技术来实现我们的方法中讨论的可靠性，可维护性和可执行性分析工具。此外，我们通过使用在实际项目中观察到的故障大数据来显示实现的应用软件的几个性能示例。

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来源
《International Journal of Performability Engineering》 |2019年第5期|共8页
作者
Yoshinobu Tamura; Shigeru Yamada;
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作者单位

Tokyo City University;

Tottori University;

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原文格式 PDF
正文语种 eng
中图分类工程设计与测绘;
关键词
Open source software; Software tool; Reliability; Maintainability; Performability; Fault big data; Deep learning;

机译：开源软件;软件工具;可靠性;可维护性;可表现;故障大数据;深度学习;

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专利

1. Fault Big Data Analysis Tool based on Deep Learning [J] . Yoshinobu Tamura, Shigeru Yamada International Journal of Performability Engineering . 2019,第5期

机译：基于深度学习的故障大数据分析工具
2. Data-driven and deep learning-based detection and diagnosis of incipient faults with application to electrical traction systems [J] . Chen Hongtian, Jiang Bin, Zhang Tianyi, Neurocomputing . 2020,第Jul5期

机译：基于数据驱动和深度学习的检测和诊断初始故障，应用于电气牵引系统
3. Towards emotion-sensitive learning cognitive state analysis of big data in education: deep learning-based facial expression analysis using ordinal information [J] . Computing . 2020,第3期

机译：面向教育中大数据的情感敏感学习认知状态分析：使用序数信息的基于深度学习的面部表情分析
4. Fault Identification Tool Based on Deep Learning for Fault Big Data [C] . Yoshinobu Tamura, Satoshi Ashida, Shigeru Yamada 2016 International Conference on Information Science and Security . 2016

机译：基于深度学习的故障大数据故障识别工具
5. Optimal algorithms for L1-norm Principal Component Analysis: New tools for signal processing and machine learning with few and/or faulty training data. [D] . Markopoulos, Panagiotis. 2015

机译：L1-norm主成分分析的最佳算法：信号处理和机器学习的新工具，培训数据很少和/或有问题。
6. A Novel Deep Learning Method for Intelligent Fault Diagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichannel Data Fusion [O] . Wenfeng Gong, Hui Chen, Zehui Zhang, 2019

机译：基于改进的CNN-SVM和多通道数据融合的旋转机械智能故障诊断深度学习新方法
7. Fault Big Data Analysis Tool based on Deep Learning [O] . Yoshinobu Tamura 2019

机译：基于深度学习的故障大数据分析工具

Fault Big Data Analysis Tool based on Deep Learning

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