Fault Identification Tool Based on Deep Learning for Fault Big Data
Yoshinobu Tamura, Satoshi Ashida, Shigeru Yamada · 2016
Many open source software (OSS) are developed under the OSS projects all over the world. Then, the software faults detected in OSS projects are managed by the bug tracking systems. Also, many data sets are recorded on the bug tracking systems by many users and project members. In this paper, we propose the useful method based on the deep learning for the improvement activities of OSS reliability. In particular, we develop an application software for visualization of fault data recorded on OSS. Moreover, several numerical illustrations of the developed application software in the actual OSS project are shown in this paper. Furthermore, we discuss the analysis results based on the developed application software by using the fault data sets of actual OSS projects.