We focus on the noise analysis by using stochastic differential equation model as training data of deep learning. Then, we use the solution process of stochastic differential equation for the software fault analysis to the training data. Also, several numerical examples are shown in this paper. Moreover, this paper shows several sensitivity analyses for the actual big fault data. © 2025 IEEE.

Previously, many researchers proposed various software reliability growth model (SRGM) to evaluate software reliability. Also, they applied SRGM to open source software (OSS). On the other hand, almost all OSS continuing to develop. In this case, OSS’s latent faults fluctuates frequently. Moreover, it is difficult to evaluate accurately because this doesn’t consider by SRGM assumptions. It is known that as the number of software users increases, the number of the detected faults tends to increase. Especially, it is seen in like the number of the users changes fluctuates and the software update. In this paper, we analyze the impact of fluctuations in the number of software users on OSS using actual data from a fault tracking system. Moreover, we review the assumptions of non-homogeneous Poisson process (NHPP) based SRGM and propose the corrected time for model considering the number of number of users. Furthermore, we applied propose model to existing general NHPP-based SRGM’s mean value function. As a result, we confirmed that the number of users increases effect to the number of the detected faults. Furthermore, the propose model can evaluate software reliability accurately. Especially, logarithmic Poisson SRGM shows the highest estimation result. © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2025.

This paper focuses on the reliability of open source software (OSS). In particular, the proposed method is based on the deep learning by using the fault big data obtained from the bug tracking system. The immune system of human body takes on the role of the protection for the pathogenic microorganisms. Recently, the source code and complexity of OSS system become large year by year. Then, we assume that the OSS fault removal system is similar to the pathogenic microorganisms. We propose the OSS fault removal system based on deep learning inspired by immune system in this paper. © 2024 IEEE.

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. copyright © International Journal of Mathematical, Engineering and Management Sciences.

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 testing-effort 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. © RQD 2023. All rights reserved.All right reserved.