Reliability and maintainability analysis and its toolbased on deep learning for fault big data
Yoshinobu Tamuraa, Shigeru Yamadab
- aTokyo City University, Tokyo, Japan
- bTottori University, Tottori-shi, Japan
Papers open inside SciRah from the original publisher or repository; SciRah never hosts them.
Abstract
Recently, many fault data sets are recorded on the bug tracking systems. These bug tracking systems are used on the Website for such as open source project management. The software managers of open source projects can comprehend the fault status by using the bug tracking system. However, these software systems for development support such as the bug tracking system cannot estimate the future trend of fault data. We discuss the methods of reliability and maintainability assessment based on the deep learning for fault big data. In particular, we develop the reliability and maintainability analysis tool based on deep learning for fault big data by using the latest programing technology. Moreover, we show several numerical illustrations of the developed software tool by using the fault big data in the actual software projects.
TL;DRThis work develops the reliability and maintainability analysis tool based on deep learning for fault big data by using the latest programing technology and shows several numerical illustrations of the developed software tool byUsing the faultBig data in the actual software projects. (Semantic Scholar)