Large Scale Fault Data Analysis and OSS Reliability Assessment Based on Quantification Method of the First Type

Machine Learning and Knowledge Extraction

Yoshinobu Tamura¹  Shigeru Yamada² 

[1] Department of Intelligent Systems, Faculty of Information Technology, Tokyo City University, Tokyo 158-8557, Japan 2 Graduate School of Engineering, Tottori University, Tottori 680-8552, Japan;Graduate School of Engineering, Tottori University, Tottori 680-8552, Japan;

关键词: fault big data;    reliability analysis;    multiple regression analysis;    quantification method;    open source project;   

DOI  :  10.3390/make2040024

来源: DOAJ

【 摘 要 】

Various big data sets are recorded on the server side of computer system. The big data are well defined as a volume, variety, and velocity (3V) model. The 3V model has been proposed by Gartner, Inc. as a first press release. 3V model means the volume, variety, and velocity in terms of data. The big data have 3V in well balance. Then, there are various categories in terms of the big data, e.g., sensor data, log data, customer data, financial data, weather data, picture data, movie data, and so on. In particular, the fault big data are well-known as the characteristic log data in software engineering. In this paper, we analyze the fault big data considering the unique features that arise from big data under the operation of open source software. In addition, we analyze actual data to show numerical examples of reliability assessment based on the results of multiple regression analysis well-known as the quantification method of the first type.

【 授权许可】

Unknown