Software reliability model for open-source software that considers the number of finite faults and dependent faults

Kwang Yoon Song; Youn Su Kim; In Hong Chang; Kwang Yoon Song; Youn Su Kim; In Hong Chang

doi:10.3934/mbe.2023524

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 7: 11785-11804. doi: 10.3934/mbe.2023524

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Software reliability model for open-source software that considers the number of finite faults and dependent faults

1.
Department of Computer Science and Statistics, Chosun University, 309 Pilmun-daero Dong-gu, Gwangju 61452, Republic of Korea
2.
Department of Computer Science and Statistics, Graduate School, Chosun University, 309 Pilmun-daero Dong-gu, Gwangju 61452, Republic of Korea

Received: 08 February 2023 Revised: 12 April 2023 Accepted: 25 April 2023 Published: 09 May 2023

Software has become a vital factor in the fourth industrial revolution. Owing to the increase in demand for software products in various fields (big data, artificial intelligence, the Internet of Things, etc.), the software industry has expanded more than ever before. Therefore, software reliability has become very important, and efforts are being made to increase it. One of these efforts is the development of software reliability models (SRMs). SRMs have been studied for a long time as a model that predicts software reliability by using the number of software faults. Software failures can occur for several reasons, including independent software faults such as code errors and software hangs, as well as dependent cases where code errors lead to other software faults. Recently, due to the diversity of software operating environments, software faults are more likely to occur in a dependent manner, and, for this reason, they are likely to increase rapidly from the beginning and progress slowly to the maximum number thereafter. In addition, many large companies have focused on open-source software (OSS) development, and OSS is being developed by many users. In this study, we propose a new SRM that considers the number of finite faults and dependent faults, and examine the goodness-of-fit of a new SRM and other existing non-homogeneous Poisson process models based on the OSS datasets. Through numerical examples, the proposed model demonstrated a significantly better goodness-of-fit when compared to other existing models, and it also exhibited better results on the newly proposed integrated criteria.
- differential equation,
- non-homogeneous Poisson process,
- software reliability,
- dependent faults,
- open-source software
Citation: Kwang Yoon Song, Youn Su Kim, In Hong Chang. Software reliability model for open-source software that considers the number of finite faults and dependent faults[J]. Mathematical Biosciences and Engineering, 2023, 20(7): 11785-11804. doi: 10.3934/mbe.2023524

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Abstract

Software has become a vital factor in the fourth industrial revolution. Owing to the increase in demand for software products in various fields (big data, artificial intelligence, the Internet of Things, etc.), the software industry has expanded more than ever before. Therefore, software reliability has become very important, and efforts are being made to increase it. One of these efforts is the development of software reliability models (SRMs). SRMs have been studied for a long time as a model that predicts software reliability by using the number of software faults. Software failures can occur for several reasons, including independent software faults such as code errors and software hangs, as well as dependent cases where code errors lead to other software faults. Recently, due to the diversity of software operating environments, software faults are more likely to occur in a dependent manner, and, for this reason, they are likely to increase rapidly from the beginning and progress slowly to the maximum number thereafter. In addition, many large companies have focused on open-source software (OSS) development, and OSS is being developed by many users. In this study, we propose a new SRM that considers the number of finite faults and dependent faults, and examine the goodness-of-fit of a new SRM and other existing non-homogeneous Poisson process models based on the OSS datasets. Through numerical examples, the proposed model demonstrated a significantly better goodness-of-fit when compared to other existing models, and it also exhibited better results on the newly proposed integrated criteria.

References

[1]	O. Khlystova, Y. Kalyuzhnova, M, Belitski, The impact of the COVID-19 pandemic on the creative industries: A literature review and future research agenda, J. Bus. Res., 139 (2022), 1192–1210. https://doi.org/10.1016/j.jbusres.2021.09.062 doi: 10.1016/j.jbusres.2021.09.062
[2]	Y. K. Dwivedi, D. L. Hughes, C. Coombs, I. Constantiou, Y. Duan, J. S. Edwards, et al., Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life, Int. J. Inf. Manage., 55 (2020), 102211. https://doi.org/10.1016/j.ijinfomgt.2020.102211 doi: 10.1016/j.ijinfomgt.2020.102211
[3]	M. Marabelli, E. Vaast, J. L. Li, Preventing the digital scars of COVID-19, Eur. J. Inform. Syst., 30 (2021), 176–192. https://doi.org/10.1080/0960085X.2020.1863752 doi: 10.1080/0960085X.2020.1863752
[4]	V. D. Soni, Information technologies: Shaping the World under the pandemic COVID-19, J. Eng. Sci., 11 (2020), 771–776. https://ssrn.com/abstract = 3634361
[5]	C. Huang, X. Zhou, X. Ran, J. Wang, H. Chen, W. Deng, Adaptive cylinder vector particle swarm optimization with differential evolution for UAV path planning, Eng. Appl. Artif. Intell., 121 (2023), 105942. https://doi.org/10.1016/j.engappai.2023.105942 doi: 10.1016/j.engappai.2023.105942
[6]	J. Xu, Y. Zhao, H. Chen, W. Deng, ABC-GSPBFT: PBFT with grouping score mechanism and optimized consensus process for flight operation data-sharing, Inf. Sci., 624 (2023), 110–127. https://doi.org/10.1016/j.ins.2022.12.068 doi: 10.1016/j.ins.2022.12.068
[7]	L. Goel, K. Okumoto, Time-dependent error-detection rate model for software reliability and other performance measures, IEEE Trans. Reliab., 28 (1979), 206–211. 10.1109/TR.1979.5220566
[8]	S. Yamada, K. Tokuno, S. Osaki, Imperfect debugging models with fault introduction rate for software reliability assessment, Int. J. Syst. Sci., 23 (1992), 2241–2252. https://doi.org/10.1080/00207729208949452 doi: 10.1080/00207729208949452
[9]	H. Pham, X. Zhang, An NHPP software reliability model and its comparison, Int. J. Reliab. Qual. Saf. Eng., 4 (1997), 269–282. https://doi.org/10.1142/S0218539397000199 doi: 10.1142/S0218539397000199
[10]	Q. Li, H. Pham, Modeling software fault-detection and fault-correction processes by considering the dependencies between fault amounts, Appl. Sci.-Basel., 11 (2021), 6998. https://doi.org/10.3390/app11156998 doi: 10.3390/app11156998
[11]	L. Pham, H. Pham, Software reliability models with time-dependent hazard function based on Bayesian approach, IEEE Trans. Syst. Man Cybern. Paart A-Syst. Hum., 30 (2000), 25–35. 10.1109/3468.823478
[12]	D. H. Lee, I. H. Chang, H. Pham, Software reliability model with dependent failures and SPRT, Mathematics, 8 (2020), 1366. https://doi.org/10.3390/math8081366 doi: 10.3390/math8081366
[13]	Y. S. Kim, K. Y. Song, H. Pham, I. H. Chang, A software reliability model with dependent failure and optimal release time, Symmetry-Basel, 14 (2022), 343. https://doi.org/10.3390/sym14020343 doi: 10.3390/sym14020343
[14]	X. Li, Y. F. Li, M. Xie, S. H. Ng, Reliability analysis and optimal version-updating for open source software, Inf. Softw. Technol., 53 (2011), 929–936. https://doi.org/10.1016/j.infsof.2011.04.005 doi: 10.1016/j.infsof.2011.04.005
[15]	M. Zhu, H. Pham, A multi-release software reliability modeling for open source software incorporating dependent fault detection process, Ann. Oper. Res., 269 (2018), 773–790. https://doi.org/10.1007/s10479-017-2556-6 doi: 10.1007/s10479-017-2556-6
[16]	V. B. Singh, P. K. Kapur, M. Basirzadeh, Open source software reliability growth model by considering change–point, Int. J. Inf. Technol., 4 (2012), 405–410.
[17]	J. Yang, Y. Liu, M. Xie, M. Zhao, Modeling and analysis of reliability of multi-release open source software incorporating both fault detection and correction processes, J. Syst. Softw., 115 (2016), 102–110. https://doi.org/10.1016/j.jss.2016.01.025 doi: 10.1016/j.jss.2016.01.025
[18]	H. Pham, A logistic fault-dependent detection software reliability model, J. Univers. Comput. Sci., 24 (2018), 1717–1730. https://doi.org/10.3217/jucs-024-12-1717 doi: 10.3217/jucs-024-12-1717
[19]	S. A. Hossain, R. C. Dahiya, Estimating the parameters of a non-homogeneous Poisson-process model for software reliability, IEEE Trans. Reliab., 42 (1993), 604–612. https://doi.org/10.1109/24.273589 doi: 10.1109/24.273589
[20]	S. Yamada, M. Ohba, S. Osaki, S-shaped reliability growth modeling for software fault detection, IEEE Trans. Reliab., 32 (1983), 475–484. https://doi.org/10.1109/TR.1983.5221735 doi: 10.1109/TR.1983.5221735
[21]	S. Osaki, Y. Hatoyama, Inflexion S-shaped software reliability growth models, In Stochastic Models in Reliability Theory, Springer-Verlag, 1984,144–162.
[22]	S. Yamada, H. Ohtera, H. Narihisa, Software reliability growth models with testing-effort, IEEE Trans. Reliab., 35 (1986), 19–23. https://doi.org/10.1109/TR.1986.4335332 doi: 10.1109/TR.1986.4335332
[23]	H. Pham, System Software Reliability, Springer, London, 2006.
[24]	H. Pham, An imperfect-debugging fault-detection dependent-parameter software, Int. J. Autom. Comput., 4 (2007), 325–328. https://doi.org/10.1109/TR.2010.2048657 doi: 10.1109/TR.2010.2048657
[25]	K. Pillai, V. S. Nair, A model for software development effort and cost estimation, IEEE Trans. Softw. Eng., 23 (1997), 485–497. https://doi.org/10.1109/32.624305 doi: 10.1109/32.624305
[26]	M. Zhu, H. Pham, A two-phase software reliability modeling involving with software fault dependency and imperfect fault removal, Comput. Lang. Syst. Struct., 53 (2018), 27–42. https://doi.org/10.1016/j.cl.2017.12.002 doi: 10.1016/j.cl.2017.12.002
[27]	K. Y. Song, I. H. Chang, H. Pham, A testing coverage model based on NHPP software reliability considering the software operating environment and the sensitivity analysis, Mathematics, 7 (2019), 450. https://doi.org/10.3390/math7050450 doi: 10.3390/math7050450
[28]	K. Sharma, R. Garg, C. K. Nagpal, R. K. Garg, Selection of optimal software reliability growth models using a distance based approach. IEEE Trans. Reliab., 59 (2010), 266–276. https://doi.org/10.1109/TR.2010.2048657 doi: 10.1109/TR.2010.2048657
[29]	M. Anjum, M. A. Haque, N. Ahmad, Analysis and ranking of software reliability models based on weighted criteria value, Int. J. Inf. Technol. Comput. Sci., 2 (2013), 1–14. https://doi.org/10.5815/IJITCS.2013.02.01 doi: 10.5815/IJITCS.2013.02.01
[30]	H. Pham, On estimating the number of deaths related to Covid-19, Mathematics, 8 (2020), 655. https://doi.org/10.3390/math8050655 doi: 10.3390/math8050655
[31]	Q. Li, H. Pham, A testing-coverage software reliability model considering fault removal efficiency and error generation, PLoS One, 12 (2017), e0181524. https://doi.org/10.1371/journal.pone.0181524 doi: 10.1371/journal.pone.0181524

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