Abstract
With the increasing use of Open Source Software (OSS) in high speed networking, parallel processing and distributed computing, OSS has emerged as mainstream in the last decade and is now being broadly accepted even by the traditional proprietary software development companies. The major advantages of OSS over traditional software development are less development cost, availability of source code, quality and security. Software reliability—an important attribute of software quality, is defined as the probability that a software will operate free of failures or breakdown for a specified time under specified conditions (IEEE Std. 1633-2016). Investigation of Software reliability with the help of software reliability models (SRM) undertakes the estimation and prediction of the failure phenomenon of a software. In this paper we have investigated whether Non-homogeneous Poisson process (NHPP) based software reliability models fit in the big data open source software fault/bug data. We have extracted real and latest bug/fault data of Hadoop and Spark–open source big data applications, from bug tracking/management tool Jira. For this purpose, we have also compared these models on different goodness-of-fit and prediction criteria based on collected failure data to ascertain whether a best fitted model can also be a best predictor. It is found that the best model fitting the failure data is not a best predictor model.
Similar content being viewed by others
References
Apache Website (2018). https://issues.apache.org/jira/secure/Dashboard.jspa. Accessed 26 March 2018
Goel AL, Okumoto K (1979) Time dependent error detection rate model for software reliability and other performance measures. IEEE Trans Reliab 28:206211
Kapur PK, Garg RB (1992) A software reliability growth model for error removal phenomenon. Softw Eng J 7:291–294
Kapur PK, Garg RB, Kumar S (1999) Contributions to hardware and software reliability. World Scientific, Singapore
Kapur PK, Pham H, Gupta A, Jha PC (2011) Software reliability assessment with OR applications. Springer, London
Lai R, Garg M (2012) A detailed study of NHPP software reliability models. J Softw 7(6):1296–1306
Li Q, Pham H (2017) A testing-coverage software reliability model considering fault removal efficiency and error generation. PLoS ONE 12(7):e0181524. https://doi.org/10.1371/journal.pone.0181524
Li X et al (2011) Reliability analysis and optimal release-updating for open source software. Inf Softw Technol 53:929936
Lyu MR (1996) Handbook of sofware reliability engineering. IEEE Computer Society Press, Los Alamitos
Ohba M, Osaki S (1984) Inflexion S-shaped software reliability growth models, stochastic models in reliability theory. Springer, p 144162
Pham H, Nordmann L, Zhang X (1999) A general imperfect software debugging model with S-shaped fault detection rate. IEEE Trans Reliab 48:169175
Pillai K, Nair VSS (1997) A model for software development effort and cost estimation. IEEE Transactions on Software Engineering 23(8):485–497
Rahmani C et al (2010) A comparative analysis of open source software reliability. J Softw 5(12):1384–1394
Rahmani C et al (2010) Exploratory failure analysis of open source software. In: 2nd International conference on software technology and engineering (ICSTE). IEEE Explore, vol 2, pp 51–56
Raymond ES (1999) The cathedral and the bazaar: musings on open source by an acci-dental revolutionary. OReilly & Associates, Sebastopol
Rossi B et al (2010) Modelling failures occurrences of open source software with reliability growth. J Open Source Softw New Horizons 268–280
Singh VB, Sujata K, Kapur PK (2010) A reliability growth model for object oriented software developed under concurrent distributed development environment. In: Proceedings of 2nd international conference on reliability safety and hazard, IEEE, pp 479–484
Singh VB, Kapur PK, Tandon A (2010) Measuring reliability growth of open source software using stochastic differential equations. In: Proceedings of second world congress on software engineering (WCSE) at Huwan, China, IEEE Xplore
Singh VB, Kapur PK, R Kumar (2010) Developing S-shaped software reliability growth model for open source software. In: Proceedings of IASTED international conference on software engineering at Austria, ACTA Press
SinghVB, Singh OP, Kumar R, Kapur PK (2010) A generalized reliability growth model for open source software. In: Proceedings of 2nd international conference on reliability safety and hazard, IEEE, pp 523–528
Stallman R (1998) The GNU project. http://www.gnu.org/gnu/thegnuproject.html
IEEE Std 1633-2008 (2008) IEEE recommended practice in software reliability
Syed-Mohamad SM et al (2008) Reliability growth of open source software using defect analysis. In: International conference on computer science and software engineering
Tamura Y, Yamada S (2009) Optimization analysis for reliability assessment based on stochastic differential equation modeling for open source software. Int J Syst Sci 40(4):429–438
Tamura Y, Yamada S (2010) Performance evaluation of reliability assessment method based on stochastic differential equation model for large-scale open source solution. Int J Syst Assur Eng Manag 1(4):324–329
van de Joode RW, de Bruijne M (2006) The organization of open source communities: towards a framework to analyze the relationship between openness and reliability. In: Proceedings of 39th Hawaii international conference on system sciences, pp 1–6
Ven K, Verelst J, Mannaert H (1998) Should you adopt open source soft-ware? IEEE Softw 25(3):54–59
Yamada S (2014) Software reliability modelling: fundamental and applications. Springer, Tokyo/Hiedelberg
Yamada S (2017) Elementary software reliability growth modelling. In: 6th International conference on reliability, Infocom technologies and optimization (ICRITO), Noida, pp 2–10
Yamada S, Tamura Y (2016) OSS Reliability Measurement and Assessment. Springer, Cham ISBN: 978-3-319-31818-9
Yamada S, Ohba M, Osaki S (1983) S-shaped reliability growth modeling for software fault detection. IEEE Trans Reliab 32:475484
Zhou Y et al (2005) Open source software reliability model: an empirical method. In: ACM SIGSOFT software engineering notes
Zou F et al (2008) Analyzing and modeling open source software bug report data. In: 19th Australian conference on software engineering
Zou F, Davis J (2008) Analyzing and modeling open source software bug report data. In: 19th Australian conference on software, IEEE Computer Society. https://doi.org/10.1109/ASWEC.2008.13
Acknowledgements
The authors are very grateful and thankful to the anonymous Referees and the Editor, whose comments have helped greatly in the presentation of this manuscript. The authors gratefully acknowledge the assistance of Professor Priya Ranjan, Department of Electrical and Electronics Engineering, Amity University, Noida in providing valuable inputs.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Kumar, R., Kumar, S. & Tiwari, S.K. A study of software reliability on big data open source software. Int J Syst Assur Eng Manag 10, 242–250 (2019). https://doi.org/10.1007/s13198-019-00777-x
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-019-00777-x