基于深度学习的故障大数据故障识别工具

Y. Tamura, Satoshi Ashida, S. Yamada
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引用次数: 5

摘要

世界上许多开源软件(OSS)都是在OSS项目下开发的。然后,通过bug跟踪系统对OSS项目中检测到的软件故障进行管理。此外,许多用户和项目成员在bug跟踪系统中记录了许多数据集。本文提出了一种基于深度学习的OSS可靠性改进方法。特别地,我们开发了一个应用软件,用于将记录在OSS上的故障数据可视化。此外,本文还给出了开发的应用软件在实际OSS项目中的几个数值实例。在此基础上,利用实际OSS项目的故障数据集,讨论了基于开发的应用软件的分析结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fault Identification Tool Based on Deep Learning for Fault Big Data
Many open source software (OSS) are developed under the OSS projects all over the world. Then, the software faults detected in OSS projects are managed by the bug tracking systems. Also, many data sets are recorded on the bug tracking systems by many users and project members. In this paper, we propose the useful method based on the deep learning for the improvement activities of OSS reliability. In particular, we develop an application software for visualization of fault data recorded on OSS. Moreover, several numerical illustrations of the developed application software in the actual OSS project are shown in this paper. Furthermore, we discuss the analysis results based on the developed application software by using the fault data sets of actual OSS projects.
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