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A software reliability model incorporating martingale process with gamma-distributed environmental factors.

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Title: A software reliability model incorporating martingale process with gamma-distributed environmental factors.
Authors: Zhu, Mengmeng1 (AUTHOR) qimengzhu@gmail.com, Pham, Hoang1 (AUTHOR)
Source: Annals of Operations Research. Nov2025, Vol. 354 Issue 3, p1389-1410. 22p.
Subjects: Software reliability, Stochastic processes, Martingales (Mathematics), Prediction models, Open source software, Gamma distributions, Fault diagnosis
Abstract: As the increasing application of software system in various industry, software reliability gains more attention from the researchers and practitioners in the past few decades. The goal of such an expanding application of software system is to continuously bring convenience and functionality in everyday life. Lots of environmental factors defined by many studies may have positive/negative impact on software reliability during the development process (Zhu et al. in J Syst Softw 109:150–160, 2015; Clarke and O'Connor in Inf Softw Technol 54(5):433–447, 2012; Zhu and Pham in J Syst Softw 1–18, 2017b). However, most existing software reliability models have not incorporated these environmental factors in the model consideration. In this paper, we propose a theoretic software reliability model incorporating the fault detection process is a stochastic process due to the randomness caused by the environmental factors. The environmental factor, Percentage of Reused Modules, is described as a gamma distribution in this study based on the collected data from industry. Open Source Software project data are included to demonstrate the effectiveness and predictive power of the proposed model. [ABSTRACT FROM AUTHOR]
Copyright of Annals of Operations Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A software reliability model incorporating martingale process with gamma-distributed environmental factors.
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  Data: <searchLink fieldCode="AR" term="%22Zhu%2C+Mengmeng%22">Zhu, Mengmeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> qimengzhu@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Pham%2C+Hoang%22">Pham, Hoang</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Annals+of+Operations+Research%22">Annals of Operations Research</searchLink>. Nov2025, Vol. 354 Issue 3, p1389-1410. 22p.
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  Data: As the increasing application of software system in various industry, software reliability gains more attention from the researchers and practitioners in the past few decades. The goal of such an expanding application of software system is to continuously bring convenience and functionality in everyday life. Lots of environmental factors defined by many studies may have positive/negative impact on software reliability during the development process (Zhu et al. in J Syst Softw 109:150–160, 2015; Clarke and O'Connor in Inf Softw Technol 54(5):433–447, 2012; Zhu and Pham in J Syst Softw 1–18, 2017b). However, most existing software reliability models have not incorporated these environmental factors in the model consideration. In this paper, we propose a theoretic software reliability model incorporating the fault detection process is a stochastic process due to the randomness caused by the environmental factors. The environmental factor, Percentage of Reused Modules, is described as a gamma distribution in this study based on the collected data from industry. Open Source Software project data are included to demonstrate the effectiveness and predictive power of the proposed model. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Annals of Operations Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10479-018-2951-7
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        Text: English
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        PageCount: 22
        StartPage: 1389
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      – SubjectFull: Software reliability
        Type: general
      – SubjectFull: Stochastic processes
        Type: general
      – SubjectFull: Martingales (Mathematics)
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      – SubjectFull: Prediction models
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      – SubjectFull: Open source software
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      – SubjectFull: Gamma distributions
        Type: general
      – SubjectFull: Fault diagnosis
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              Text: Nov2025
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              Y: 2025
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