TITLE:
Reliability Assessment Tool Based on Deep Learning and Data Preprocessing for OSS
AUTHORS:
Shoichiro Miyamoto, Yoshinobu Tamura, Shigeru Yamada
KEYWORDS:
Open Source Software, Deep Learning, Software Reliability, Deep Learning, Software Tool
JOURNAL NAME:
American Journal of Operations Research,
Vol.12 No.3,
May
24,
2022
ABSTRACT: Recently, many open source software (OSS) developed by various OSS
projects. Also, the reliability assessment methods of OSS have been proposed by
several researchers. Many methods for software reliability assessment have been
proposed by software reliability growth models. Moreover, our research group has
been proposed the method of reliability assessment for the OSS. Many OSS use
bug tracking system (BTS) to manage software faults after it released. It keeps
a detailed record of the environment in terms of the faults. There are several
methods of reliability assessment based on deep learning for OSS fault data in
the past. On the other hand, the data registered in BTS differences depending
on OSS projects. Also, some projects have the specific collection data. The BTS
has the specific collection data for each project. We focus on the recorded
data. Moreover, we investigate the difference between the general data and the
specific one for the estimation of OSS reliability. As a result, we show that
the reliability estimation results by using specific data are better than the method using general data. Then, we show the
characteristics between the specified data and general one in this paper. We
also develop the GUI-based software to perform these reliability analyses so
that even those who are not familiar with deep learning implementations can
perform reliability analyses of OSS.