Deep Learning Method Based on Fault Big Data Analysis for OSS Reliability Assessment

Yoshinobu Tamura () and Shigeru Yamada ()
Additional contact information
Yoshinobu Tamura: Yamaguchi University
Shigeru Yamada: Tottori University

Chapter Chapter 8 in Applied OSS Reliability Assessment Modeling, AI and Tools, 2024, pp 81-149 from Springer

Abstract: Abstract Many OSS are useful for many software engineers and general users around the world. However, there is no established standard method of quality/reliability assessment for OSS. The bug tracking system is well known as the useful system for quality improvement of OSS. The bug tracking system is implemented in various open source projects in recent years. Also, various fault data sets are registered on the database of bug tracking system.

Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-3-031-64803-8_8

Ordering information: This item can be ordered from
http://www.springer.com/9783031648038

DOI: 10.1007/978-3-031-64803-8_8

Access Statistics for this chapter

More chapters in Springer Series in Reliability Engineering from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().