Abstract
Open source software reliability is an important factor affecting the quality of open source software. The developed reliability models of open source software cannot meet the actual evaluation of open source software reliability because of the complexity, dynamics, and uncertainty of open source software development. Considering the dynamic changes of fault introduction in open source software development, we propose an open source software reliability model with fault introduction based on the generalized Pareto distribution. We use three Apache open source software projects to validate the proposed model. Least squares estimation is used to estimate the model parameter values. Experimental results indicate that the proposed model has better fitting and predictive performance than other existing models. The generalized Pareto distribution of the fault introduction is consistent with that in actual open source software development. Thus, the proposed model can assist developers and managers in evaluating the reliability of open source software in the actual process of open source software development.
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Raymond, E.S.: The cathedral and the bazaar: musings on linux and open source by an accidental revolutionary, p. 2. Sebastopol, O’Reilly (2001)
Michlmayr, M.; Fitzgerald, B.; Stol, K.J.: Why and how should open source projects adopt time-based releases? IEEE Softw. 32(2), 55–63 (2015)
Singh, V.B.; Singh, G.P.; Kumar, R. et al.: A generalized reliability growth model for open source software. In: International Conference on Reliability. IEEE (2011)
Li, X.; Li, Y.F.; Xie, M.; Ng, S.H.: Reliability analysis and optimal version-updating for open source software. Inf. Softw. Technol. 53(9), 929–936 (2011)
Huang, C.Y.; Kuo, C.S.; Luan, S.P.: Evaluation and application of bounded generalized pareto analysis to fault distributions in open source software. IEEE Trans. Reliab. 63(1), 309–319 (2014)
Liu, Y.; Xie, M.; Yang, J. et al.: A new framework and application of software reliability estimation based on fault detection and correction processes. In: 2015 IEEE international conference on software quality, reliability and security. IEEE (2015)
Yang, J.; Yu, L.; Min, X., et al.: Modeling and analysis of reliability of multi-release open source software incorporating both fault detection and correction processes. J. Syst. Softw. 115(C), 102–110 (2016)
Singh, V.B.; Sharma, M.; Pham, H.: Entropy based software reliability analysis of multi-version open source software. IEEE Trans. Softw. Eng. 44, 1207–1223 (2018)
Zhu, M.; Pham, H.: A multi-release software reliability modeling for open source software incorporating dependent fault detection process. Ann. Oper. Res. 2, 1–18 (2017)
Wang, J.; Mi, X.: Open source software reliability model with the decreasing trend of fault detection rate. Comput. J. 62(9), 1301–1312 (2018)
Raghuvanshi, K.K.; Sharma, M.; Tandon, A. et al.: Quantitative quality assessment of open source software by considering new features and feature improvements. computational science and its applications—ICCSA 2018. Springer, Cham (2018).
Andersson, D.; Runeson, P.: A replicated quantitative analysis of fault distributions in complex software systems. IEEE Trans. Softw. Eng. 33(6), 273–286 (2007)
Zhou, Y.; Davis, J.: Open source software reliability model: an empirical approach. In: Proceedings of the Fifth Workshop on Open Source Software Engineering. New York: ACM, 2005: 1–6.
Goel, A.L.; Okumoto, K.: Time-dependent error-detection rate model for software reliability and other performance measures. IEEE Trans. Reliab. R28, 206–211 (1979)
Yamada, S.; Ohba, M.; Osaki, S.: S-shaped reliability growth modeling for software error detection. IEEE Trans. Reliab. R-32, 475–484 (1983)
Ohba, M.: Inflection S-shaped software reliability growth model. Stochastic models in reliability theory, p. 144–162. Springer, Berlin (1984)
Yamada, S.; Tokuno, K.; Osaki, S.: Imperfect debugging models with fault introduction rate for software reliability assessment. Int. J. Syst. Sci. 23(12), 2241–2252 (1992)
Pham, H.; Zhang, X.: An NHPP software reliability models and its comparison. Int. J. Rel. Qual. Saf. Eng. 14(3), 269–282 (1997)
Pham, H.; Nordmann, L.; Zhang, X.M.: A general imperfect software-debugging model with S-shaped fault-detection rate. IEEE Trans. Reliab. 48(2), 169–175 (1999)
Tamura, Y.; Yamada, S.: Software reliability growth model based on stochastic differential equations for open source software. In: Proceedings of the 4th IEEE international conference on mechatronics, Kumamoto, 8–10 May 2007, CD-ROM (ThM1-C-1)
Kuo, C.S.; Huang, C.Y.; Luan, S.P.: A study of using two-parameter generalized pareto model to analyze the fault distribution of open source software. In: 2012 IEEE Sixth International Conference on Software Security and Reliability. IEEE (2012)
Luan, S.P.; Huang, C.Y.: An improved Pareto distribution for modelling the fault data of open source software. Softw. Test. Verif. Reliab. 24(6), 416–437 (2014)
Peng, R.; Li, Y.F.; Liu, Y.: Reliability of multi-release open-source software—Software Fault Detection and Correction: Modeling and Applications, p. 75–94. Springer, Singapore (2018)
Garmabaki, H.S.; Barabadi, A.; Yuan, F. et al.: Reliability modeling of successive release of software using NHPP. In: Proceedings of 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, pp. 761–766 (2015)
Nijhawan, N.; Aggarwal, A.G.; Dhaka, V.: An SRGM for multi-release open source software system. Int. J. Innov. Technol. Manag. 15(02), 18500110–1185001120 (2018)
Aggarwal, G.; Dhaka, V.; Nijhawan, N., et al.: Reliability growth analysis for multi-release open source software systems with change point. System performance and management analytics, p. 125–137. Singapore, Springer (2019)
Aggarwal, A.G.: Multi release reliability growth modeling for open source software under imperfect debugging. System performance and management analytics, p. 77–86. Singapore, Springer (2019)
Tandon, A.; Aggarwal, A.G.: Testing coverage based reliability modeling for multi-release open-source software incorporating fault reduction factor. Life Cycle Reliab. Saf. Eng. 9, 425–435 (2020)
Pickands, J.: Statistical inference using extreme order statisics. Ann. Stat. 3, 119–131 (1975)
Hosking, J.R.M.; Wallis, J.R.: Parameter and quantile estimation for the generalized pareto distribution. Technometrics 29(3), 339–349 (1987)
Sharma, K.; Garg, R.; Nagpal, C.K., et al.: Selection of optimal software reliability growth models using a distance based approach. IEEE Trans. Reliab. 59(2), 266–276 (2010)
Huang, C.Y.; Lyu, M.R.: Estimation and analysis of some generalized multiple change-point software reliability models. IEEE Trans. Reliab. 60(2), 498–514 (2011)
Castillo, E.; Hadi, A.S.: Fitting the generalized pareto distribution to data. Publ. Am. Stat. Assoc. 92(440), 1609–1620 (1997)
Erto, P.; Giorgio, M.; Lepor, A.: The generalized inflection S-shaped software reliability growth model. IEEE Trans. Reliab. 69(1), 228–244 (2018)
Goel, A.L.: Software reliability models: assumptions, limitations and applicability. IEEE Trans. Softw. Eng. SE-11(12), 1411–1423 (1985)
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This work is supported by the Natural Science Foundation of Shanxi Province of China under Grant No. 201801D121120.
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Appendix A
Appendix A
Given G(t) = \( \int_{0}^{t} {B(x){\text{d}}x} \) as B(x) = \( \frac{b}{{1 + \mu \exp ( - bx)}} \), then
\( \exp [G(t)] = \frac{{\mu + \exp (bt)}}{{1 + \mu }} \).
Equation (A.1) can be transferred as follows:
Expansion according to Taylor's formula
then
When t = 0, M(t) = 0.
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Wang, J. Model of Open Source Software Reliability with Fault Introduction Obeying the Generalized Pareto Distribution. Arab J Sci Eng 46, 3981–4000 (2021). https://doi.org/10.1007/s13369-021-05382-4
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DOI: https://doi.org/10.1007/s13369-021-05382-4