Username   Password       Forgot your password?  Forgot your username? 

 

Fault Big Data Analysis Tool based on Deep Learning

Volume 15, Number 5, May 2019, pp. 1289-1296
DOI: 10.23940/ijpe.19.05.p4.12891296

Yoshinobu Tamuraa,* and Shigeru Yamadab

aTokyo City University, Tamazutsumi 1-28-1, Setagaya-ku, Tokyo, 158-8557, Japan
bTottori University, Minami 4-101, Koyama, Tottori-shi, 680-8552, Japan

 

(Submitted on October 15, 2017; Revised on February 28, 2018; Accepted on March 30, 2018)

Abstract:

Software managers can obtain useful information from many fault data sets recorded on bug tracking systems (BTS). However, it is difficult to find helpful measures for software reliability, maintainability, and performability, because the data collected on the BTS are mixed with qualitative and quantitative ones. This paper discusses the methods of reliability, maintainability, and performability assessment by deep learning for big data in terms of software faults. Specifically, we implement the reliability, maintainability, and performability analysis tool discussed in our method by using the latest programing technology. Moreover, we show several performance examples of the implemented application software by using the fault big data observed in the practical projects.

 

References: 12

    1. S. Yamada, “Software Reliability Modeling: Fundamentals and Applications,” Springer-Verlag, Tokyo/Heidelberg, 2014
    2. P. K. Kapur, H. Pham, A. Gupta, and P.C. Jha, “Software Reliability Assessment with OR Applications,” Springer-Verlag, London, 2011
    3. S. Yamada and Y. Tamura, “OSS Reliability Measurement and Assessment,” Springer International Publishing, Switzerland, 2016
    4. D. P. Kingma, D. J. Rezende, S. Mohamed, and M. Welling, “Semi-Supervised Learning with Deep Generative Models,” in Proceedings of Neural Information Processing Systems, 2014
    5. E. D. George, Y. Dong, D. Li, and A. Alex, “Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition,” IEEE Transactions on Audio, Speech, and Language Processing, Vol. 20, No. 1, pp. 30-42, 2012
    6. B. Hutchinson, L. Deng, and D. Yu, “Tensor Deep Stacking Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, No. 8, pp. 1944-1957, 2013
    7. NW.js community, NW.js, http://nwjs.io/
    8. The R Project for Statistical Computing, The R Foundation, https://www.r-project.org/
    9. Plotly R Library, Plotly, https://plot.ly/r/
    10. Y. Tamura and S. Yamada, “Reliability Analysis based on Deep Learning for Fault Big Data on Bug Tracking System,” in Proceedings of the IEEE International Conference on Reliability, Infocom Technology and Optimization, pp. 37-42, Noida, India, September 7-9, 2016
    11. Y. Tamura and S. Yamada, “Reliability and Maintainability Analysis and its Tool based on Deep Learning for Fault Big Data,” in Proceedings of the IEEE International Conference on Reliability, Infocom Technology and Optimization, pp. 104-109, Noida, India, September 20-22, 2017
    12. The Apache Software Foundation, The Apache HTTP Server Project, http://httpd.apache.org/

     

    Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

     
    This site uses encryption for transmitting your passwords. ratmilwebsolutions.com