Abstract
Changes in software source codes are unavoidable and source code of software is repetitively modified to meet user’s huge requirements. Largely there are three types of code changes occur in the source code namely, bug repair, feature enhancement and addition of new features. The change due to bug fix, improvement and accumulation of new features brings uncertainties in the bug removal rate. In the present work, these uncertainties have been explicitly modeled and using three-dimensional wiener processes that define the three types of fluctuation; we have come up with an entropy prediction modelling framework. The analytical solution of the equation is interpreted using Itô’s process. The models are fitted on three real life projects namely Avro, Hive and Pig of Apache open source software. The experimental findings show that present models exhibit accurate estimation results and have strong prediction skills.
Similar content being viewed by others
References
Anand A, Singhal S, Singh O (2018a) Revisiting dynamic potential adopter diffusion models under the influence of irregular fluctuations in adoption rate. In: Handbook of research on promoting business process improvement through inventory control techniques. 499–519. IGI Global
Anand A, Deepika, Verma AK, Ram M (2018) Revisiting error generation and stochastic differential equation-based software reliability growth models. System reliability management. CRC Press, Boca Raton, pp 65–78
Anand A, Bharmoria S, Ram M (2019) Characterizing the complexity of code changes in open source software. Recent advancements in software reliability assurance. Taylor & Francies Group, Milton Park
Anand A, Deepika, Singh O (2019) Formulation of error generation-based SRGMs under the influence of irregular fluctuations. System performance and management analytics. Springer, Singapore, pp 103–117
Arisholm E, Briand LC (2006) Predicting fault-prone components in a java legacy system. In: Proceedings of the 2006 ACM/IEEE international symposium on empirical software engineering, 8–17. ACM
Arora HD, Kumar V, Sahni R (2014) Study of bug prediction modelling using various entropy measures: a theoretical approach. In: 3rd international conference on reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions), 1–5. IEEE
Chaturvedi KK, Bedi P, Misra S, Singh VB (2013) An empirical validation of the complexity of code changes and bugs in predicting the release time of open source software. In: 2013 IEEE 16th international conference on computational science and engineering (CSE), 1201–1206. IEEE
Chaturvedi KK, Kapur PK, Anand S, Singh VB (2014) Predicting the complexity of code changes using entropy based measures. Int J Syst Assur Eng Manage 5(2):155–164
D'Ambros M, Lanza M, Robbes R (2010) An extensive comparison of bug prediction approaches. In: 2010 7th IEEE working conference on mining software repositories (MSR 2010), 31–41. IEEE
D’Ambros M, Lanza M, Robbes R (2012) Evaluating defect prediction approaches: a benchmark and an extensive comparison. Empir Softw Eng 17(4–5):531–577
Fenton NE, Neil M (1999) A critique of software defect prediction models. IEEE Trans Softw Eng 25(5):675–689
Graves TL, Karr AF, Marron JS, Siy H (2000) Predicting fault incidence using software change history. IEEE Trans Softw Eng 26(7):653–661
Hassan AE (2009) Predicting faults using the complexity of code changes. In: Proceedings of the 31st international conference on software engineering, 78–88. IEEE Computer Society
https://opensource.com/resources/what-open-source accessed 28 March 2019
Jeon CK, Byun C, Kim NH, In HP (2014) An entropy based method for defect prediction in software product lines. Int J Multimed Ubiquitous Eng 9(3):375–377
Kafura D, Reddy GR (1987) The use of software complexity metrics in software maintenance. IEEE Trans Softw Eng 3:335–343
Kamavaram S, Goseva-Popstojanova K (2002) Entropy as a measure of uncertainty in software reliability. In: 13th international symposium software reliability engineering, 209–210
Kamei Y, Matsumoto S, Monden A, Matsumoto KI, Adams B, Hassan AE (2010, September) Revisiting common bug prediction findings using effort-aware models. In: 2010 IEEE international conference on software maintenance, 1–10. IEEE
Kapur PK, Singh O, Singh J (2011a) Stochastic differential equation based software reliability growth modelling with change point and two types of imperfect debugging. In: Proceedings of 5th national conference on computing for nation development, Bharti Vidyapeeth’s Institute of Computer Application and Management, New Delhi, INDIACom, 605–612
Kapur PK, Pham H, Gupta A, Jha PC (2011) Software reliability assessment with OR applications. Springer, London
Khoshgoftaar TM, Allen EB, Jones WD, Hudepohl JP (1999) Data mining for predictors of software quality. Int J Softw Eng Knowl Eng 9(05):547–563
Leszak M, Perry DE, Stoll D (2002) Classification and evaluation of defects in a project retrospective. J Syst Softw 61(3):173–187
Moser R, Pedrycz W, Succi G (2008) A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction. In: Proceedings of the 30th international conference on Software engineering, 181–190. ACM
Nagappan N, Ball T (2005) Use of relative code churn measures to predict system defect density. In: Proceedings of the 27th international conference on Software engineering, 284–292. ACM
Oksendal B (2013) Stochastic differential equations: an introduction with applications. Springer, Berlin
SAS Institute Inc (2004) SAS/ETS user’s guide version 9.1. Cary, NC: SAS Institute Inc
Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423
Shannon CE (1951) Prediction and entropy of printed English. Bell Syst Tech J 30(1):50–64
Singh VB, Chaturvedi KK (2012) Entropy based bug prediction using support vector regression. In: 2012 12th international conference on intelligent systems design and applications (ISDA), 746–751. IEEE
Singh VB, Chaturvedi KK (2013) Improving the quality of software by quantifying the code change metric and predicting the bugs. International conference on computational science and its applications. Springer, Berlin, Heidelberg, pp 408–426
Singh VB, Sharma M (2014) Prediction of the complexity of code changes based on number of open bugs, new feature and feature improvement. In: IEEE international symposium on software reliability engineering workshops, 478–483
Singh VB, Chaturvedi KK, Khatri SK, Kumar V (2015) Bug prediction modelling using complexity of code changes. Int J Syst Assur Eng Manag 6(1):44–60
Tamura Y, Yamada S (2014) 3D software tool for reliability assessment based on three dimensional wiener process model considering big data on cloud computing. In: International conference on reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions), AIIT, Amity University Uttar Pradesh, 33–37
Yamada S, Nishigaki A, Kimura M (2003) A stochastic differential equation model for software reliability assessment and its goodness-of-fit. Int J Reliab Appl 4(1):1–11
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
Deepika, Anand, A., Singh, O. et al. Three-dimensional wiener process based entropy prediction modelling for OSS. Int J Syst Assur Eng Manag 12, 188–198 (2021). https://doi.org/10.1007/s13198-020-01040-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-020-01040-4