Estimation and comparison of mean time between failures based on deep learning for OSS fault big data

Yoshinobu Tamura (), Ryota Ueki (), Adarsh Anand () and Shigeru Yamada ()
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Yoshinobu Tamura: Yamaguchi University
Ryota Ueki: Tokyo City University
Adarsh Anand: University of Delhi
Shigeru Yamada: Tottori University

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 8, No 4, 3596-3611

Abstract: Abstract We discuss the estimation of time between software failures for the assessment of operation performance of open source software (OSS). Then, we compare the estimated values of the estimation methods of mean time between software failures based on deep learning. We apply the deep feed-forward neural network in order to estimate the mean time between software failures. In particular, we discuss the situation of learning according to the amount of learning data sets. Thereby, we can show the practicability of the proposed method for fault big scale data based on deep learning from the standpoint of OSS project management. Moreover, we analyze actual data to show various numerical examples based on several cases of learning data sets. Furthermore, we compare the proposed method with the conventional hazard rates.

Keywords: Fault big data; Mean time between software failures; Deep learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13198-023-01907-2

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