Deep Learning Approach for OSS Reliability Assessment Considering Fault Modification  
Author Yoshinobu Tamura


Co-Author(s) Shigeru Yamada


Abstract This paper focuses on the fault big data of open source software (OSS). The fault detection phenomenon depends on the maintenance effort, because the number of software fault is influenced by the effort expenditure. Actually, the software reliability growth models with testingeffort have been proposed in the past. In this paper, we apply the deep learning approach to the OSS fault big data. Then, we show several reliability assessment measures based on the deep learning. Moreover, several numerical illustrations based on the proposed deep learning model are shown in this paper.


Keywords Open source software, deep learning, reliability, fault modification
    Article #:  RQD28-260

Proceedings of 28th ISSAT International Conference on Reliability & Quality in Design
August 3-5, 2023