A Study of the Extraction of Bug Judgment and Correction Times from Open Source Software Bug Logs

Razzaq, Seher; Li, Yan‐Fu; Xie, Min

doi:10.1109/qrs-c.2018.00050

Cited by 6 publications

(2 citation statements)

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“…Several research papers on reliability/quality evaluation approaches for open-source products have been proposed in the past (Li et al, 2005;Anbalagan and Vouk, 2008;Lee et al, 2008;Syed-Mohamad and McBride, 2008;Yadav et al, 2011;Razzaq et al, 2018;Rammohan and Rajesh, 2020). These papers are the reliability/quality evaluation approach by using.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The open-source product is useful for many software companies to make a standardization and cost reduction of development. On the other hand, the open-source product has several quality problems, because there is no specific testing phase (Li et al, 2005;Anbalagan and Vouk, 2008;Lee et al, 2008;Syed-Mohamad and McBride, 2008;Yadav et al, 2011;Razzaq et al, 2018;Rammohan and Rajesh, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Based on Fine Tuning with Application to the Reliability Assessment of Similar Open Source Software

Tamura

Yamada

2023

Int. j. math. eng. manag. sci.

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Recently, many open-source products have been used under the situations of general software development, because the cost saving and standardization. Therefore, many open-source products are gathering attention from many software development companies. Then, the reliability/quality of open-source products becomes very important factor for the software development. This paper focuses on the reliability/quality evaluation of open-source products. In particular, the large quantity fault data sets recorded on Bugzilla of open-source products is used in many open-source development projects. Then, the large amount of data sets of software faults is recorded on the Bugzilla. This paper proposes the reliability/quality evaluation approach based on the deep machine learning by using the large quantity fault data on the Bugzilla. Moreover, the large quantity fault data sets are analyzed by the deep machine learning based on the fine-tuning.

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