Abstract
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.
Highlights
A waterfall development model is well-known as the traditional software development style
We have discussed the quantification method of the first type for the fault recorded on the bug tracking system of open source software (OSS)
We have found that the proposed method can assess the important factors in terms of the OSS quality control by using the multiple regression analysis
Summary
A waterfall development model is well-known as the traditional software development style. There are several methods based on empirical data analysis [4,5] It is very useful for the OSS developers to understand the trend of fault big data recorded on the OSS bug tracking system from the standpoint of bird’s-eye view. Many software reliability assessment methods based on the stochastic model have been proposed by several researchers [6,7,8]. Several research papers have proposed the methods in terms of the upper and lower limits based on software reliability growth models [1,2], and the empirical approach for OSS [3]. This paper proposes the method based on the statistical analysis and typical hazard rate model for the large scale fault data analysis and OSS reliability assessment. We show several analysis examples based on the proposed method by using the actual fault big data
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