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

被引:0
|
作者
Tamura, Yoshinobu [1 ]
Yamada, Shigeru [2 ]
机构
[1] Tokyo City Univ, Fac Informat Technol, Dept Intelligent Syst, Tokyo 1588557, Japan
[2] Tottori Univ, Grad Sch Engn, Tottori 6808552, Japan
来源
关键词
fault big data; reliability analysis; multiple regression analysis; quantification method; open source project; SOFTWARE; MODEL;
D O I
10.3390/make2040024
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
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.
引用
收藏
页码:436 / 452
页数:17
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