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

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.

[1]  Liang Yin,et al.  Confidence interval estimation of NHPP-based software reliability models , 1999, Proceedings 10th International Symposium on Software Reliability Engineering (Cat. No.PR00443).

[2]  Jeffrey S. Norris,et al.  Mission-critical development with open source software: lessons learned , 2004, IEEE Software.

[3]  Ying Zhou.  Open source software reliability model : an empirical approach , 2005 .

[4]  Tadashi Dohi,et al.  Variational Bayesian Approach for Interval Estimation of NHPP-Based Software Reliability Models , 2007, 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN'07).

[5]  K. M. George,et al.  A multiple regression model for trend change prediction , 2010, 2010 International Conference on Financial Theory and Engineering.

[6]  Sandro Morasca,et al.  An empirical investigation of perceived reliability of open source Java programs , 2012, SAC '12.

[7]  Olivier Togni,et al.  Statistical learning and multiple linear regression model for network selection using MIH , 2014, The Third International Conference on e-Technologies and Networks for Development (ICeND2014).

[8]  Janusz Sosnowski,et al.  Investigating software testing and maintenance reports: Case study , 2015, Inf. Softw. Technol..

[9]  Lech Madeyski,et al.  Which process metrics can significantly improve defect prediction models? An empirical study , 2014, Software Quality Journal.

[10]  Yoshinobu Tamura,et al.  Dependability Analysis Tool Considering the Optimal Data Partitioning in a Mobile Cloud , 2017 .

[11]  Yoshinobu Tamura,et al.  Open source software cost analysis with fault severity levels based on stochastic differential equation models , 2017 .

[12]  Yoshinobu Tamura,et al.  Multi-Dimensional Software Tool for OSS Project Management Considering Cloud with Big Data , 2017 .

[13]  Yoshinobu Tamura,et al.  Dependability Analysis Tool Based on Multi-Dimensional Stochastic Noisy Model for Cloud Computing with Big Data , 2017 .

[14]  Hoang Pham,et al.  Entropy Based Software Reliability Analysis of Multi-Version Open Source Software , 2018, IEEE Transactions on Software Engineering.

[15]  Xu Yanmei,et al.  Multiple Linear Regression Analysis of Real Estate Price , 2018, 2018 International Conference on Robots & Intelligent System (ICRIS).

[16]  Janusz Sosnowski,et al.  Correlating software metrics with software defects , 2018, Symposium on Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments (WILGA).

[17]  Hoang Pham,et al.  A Generalized Software Reliability Growth Model With Consideration of the Uncertainty of Operating Environments , 2019, IEEE Access.

[18]  Yoshinobu Tamura,et al.  Maintenance effort management based on double jump diffusion model for OSS project , 2019, Ann. Oper. Res..