More Web Proxy on the site http://driver.im/

research-article

Burr-type NHPP-based software reliability models and their applications with two type of fault count data

Authors:

Okamura HiroyukiAuthors Info & Claims

Volume 191, Issue C

https://doi.org/10.1016/j.jss.2022.111367

Published: 01 September 2022 Publication History

Abstract

In this paper, we summarize the so-called Burr-type software reliability models (SRMs) based on the non-homogeneous Poisson process (NHPP) and comprehensively evaluate the model performances by comparing them with the existing NHPP-based SRMs. Two kinds of software fault count data are considered; fault-detection time-domain data and fault-detection time-interval data (group data). For 8 data sets in each fault count type, we estimate the model parameters by means of the maximum likelihood estimation and evaluate the performance metrics in terms of goodness-of-fit and prediction. It is shown that the Burr-type NHPP-based SRMs could show the better performances than the existing NHPP-based SRMs in many cases. The main contribution of the paper consists in suggesting that the Burr-type NHPP-based SRMs should be the possible candidates for selecting the best SRM in terms of goodness-of-fit and predictive performances.

Highlights

•

Propose novel software reliability models based on Burr-type distributions

•

Estimate model parameters by means of maximum likelihood estimation

•

Analysis of two type of software fault count data: time-domain data and group data

•

Compare the goodness-of-fit and predictive performances with the existing models

•

Evaluate the quantitative software reliability

References

[1]

Abdel-Ghaly A.A., Al-Dayian G.R., Al-Kashkari F.H., The use of Burr type XII distribution on software reliability growth modelling, Microelectr. Reliab. 37 (2) (1997) 305–313,.

[2]

Abdel-Ghaly A.A., Chan P.Y., Littlewood B., Evaluation of competing software reliability predictions, IEEE Trans. Softw. Eng. SE-12 (9) (1986) 950–967,.

Digital Library

[3]

Achcar J.A., Dey D.K., Niverthi M., A Bayesian approach using nonhomogeneous Poisson processes for software reliability models, in: Front. Reliab., World Scientific, 1998, pp. 1–18,.

[4]

Ahmad N., Khan M.G.M., Quadri S.M.K., Kumar M., Modelling and analysis of software reliability with Burr type X testing-effort and release-time determination, J. Modell. Manag. 4 (1) (2009) 28–54,.

[5]

Ahmad N., Quadri S.M.K., Khan M.G.M., Kumar M., Software reliability growth models incorporating Burr type III test-effort and cost-reliability analysis, Int. J. Comput. Sci. Inf. Technol. 2 (1) (2011) 555–562.

[6]

An J.-H., Two model comparisons of software reliability analysis for Burr type XII distribution, J. Korean Data Inf. Sci. Soc. 23 (4) (2012) 815–823,.

[7]

Burr I.W., Cumulative frequency functions, Ann. Math. Stat. 13 (2) (1942) 215–232,.

[8]

Chen Y., Singpurwalla N.D., Unification of software reliability models by self-exciting point processes, Adv. Appl. Probab. 29 (2) (1997) 337–352,.

[9]

Chowdary C.S., Prasad R.S., Sobhana K., Burr type III software reliability growth model, IOSR J. Comput. Eng. 17 (1) (2015) 49–54,.

[10]

Goel A.L., Software reliability models: assumptions, limitations, and applicability, IEEE Trans. Softw. Eng. SE-11 (12) (1985) 1411–1423,.

Digital Library

[11]

Goel A.L., Okumoto K., Time-dependent error-detection rate model for software reliability and other performance measures, IEEE Trans. Reliab. R-28 (1979) 206–211,.

[12]

Gokhale, S.S., Trivedi, K.S., 1998. Log-logistic software reliability growth model. In: Proceedings Third IEEE International High-Assurance Systems Engineering Symposium. HASE 1998, pp. 34–41. https://doi.org/10.1109/HASE.1998.731593.

[13]

Gokhale S.S., Trivedi K.S., A time/structure based software reliability model, Ann. Softw. Eng. 8 (1) (1999) 85–121,.

Digital Library

[14]

Huang C.-Y., Lyu M.R., Kuo S.-Y., A unified scheme of some nonhomogenous Poisson process models for software reliability estimation, IEEE Trans. Softw. Eng. 29 (3) (2003) 261–269,.

Digital Library

[15]

Imanaka T., Dohi T., Software reliability modeling based on Burr XII distributions, IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 98 (10) (2015) 2091–2095,.

[16]

Islam S.F., Introducing Burr-type XII testing-effort with change point based software reliability growth model, Glob. Sci. J. 8 (8) (2020) 2712–2718.

[17]

Kim H.-C., Assessing software reliability based on NHPP using SPC, Int. J. Softw. Eng. Appl. 7 (6) (2013) 61–70,.

[18]

Kim H.-C., A study on comparative evaluation of software reliability model applying modified exponential distribution, Int. J. Eng. Res. Technol. 13 (5) (2020) 867–872,.

[19]

Kim H.-C., Park H.-K., The comparative study of software optimal release time based on Burr distribution, Int. J. Adv. Comput. Technol. 2 (3) (2010) 119–128,.

[20]

Langberg N., Singpurwalla N.D., A unification of some software reliability models, SIAM J. Sci. Stat. Comput. 6 (3) (1985) 781–790,.

Digital Library

[21]

Li, S., Dohi, T., Okamura, H., 2021. A comprehensive evaluation for Burr-type NHPP-based software reliability models. In: 2021 8th International Conference on Dependable Systems and their Applications. DSA 2021, pp. 1–12.

[22]

Lyu M.R. (Ed.), Handbook of Software Reliability Engineering, McGraw-Hill, NewYork, 1996.

[23]

Miller D.R., Exponential order statistic models of software reliability growth, IEEE Trans. Softw. Eng. SE-12 (1) (1986) 12–24,.

Digital Library

[24]

Musa J.D., Software Reliability Data, Technical Report in Rome Air Development Center, 1979.

[25]

Musa J.D., Iannino A., Okumoto K., Software Reliability - Measurement, Prediction, Application, McGraw-Hill, NewYork, 1987.

[26]

Ohba M., Inflection S-shaped software reliability growth model, in: Stochastic Models in Reliability Theory, Springer, 1984, pp. 144–162,.

[27]

Ohishi K., Okamura H., Dohi T., Gompertz software reliability model: estimation algorithm and empirical validation, J. Syst. Softw. 82 (3) (2009) 535–543,.

Digital Library

[28]

Okamura, H., Dohi, T., 2013. SRATS: software reliability assessment tool on spreadsheet (Experience report). In: 2013 IEEE 24th International Symposium on Software Reliability Engineering. ISSRE 2013, pp. 100–107. https://doi.org/10.1109/ISSRE.2013.6698909.

[29]

Okamura H., Dohi T., Phase-type software reliability model: parameter estimation algorithms with grouped data, Ann. Oper. Res. 244 (1) (2016) 177–208,.

[30]

Okamura H., Dohi T., Osaki S., Software reliability growth models with normal failure time distributions, Reliab. Eng. Syst. Saf. 116 (2013) 135–141,.

[31]

Okamura, H., Etani, Y., Dohi, T., 2011. Quantifying the Effectiveness of Testing Efforts on Software Fault Detection with a Logit Software Reliability Growth Model. In: 2011 Joint Conference of the 21st International Workshop on Software Measurement (IWSM 2011) and the 6th International Conference on Software Process and Product Measurement (MENSURA 2011). pp. 62–68. https://doi.org/10.1109/IWSM-MENSURA.2011.26.

[32]

Prasad R.S., Mohan K.V.M., Sridevi G., Assessing Burr-type XII software reliability for interval domain data using SPC, Comput. Eng. 79 (2014) 30335–30340.

[33]

Prasad R.S., Mohan K.M., Sridevi G., Burr type XII software reliability growth model, Int. J. Comput. Appl. 108 (16) (2014) 16–20,.

[34]

Prasad R.S., Mohan K.V.M., Sridevi G., Monitoring Burr type XII software quality using SPC, Int. J. Appl. Eng. Res. 9 (22) (2014) 16651–16660.

[35]

Ravikumar M.S., Kantam R.R.L., Software reliability model based on Burr-type XII distribution, Int. J. Adv. Eng. Res. Appl. 2 (9) (2017) 561–564.

[36]

Sobhana K., Prasad R.S., Burr-type III software reliability with SPC - an order statistics approach, Int. J. Res. Stud. Comput. Sci. Eng. 2 (3) (2015) 21–24.

[37]

Sridevi G., Akbar S., Burr-type X software reliability growth model, Asian J. Inf. Technol. 15 (16) (2016) 2988–2991,.

[38]

Sridevi G., Rani C.M.S., Comparison of software reliability analysis for Burr distribution, J. Theor. Appl. Inf. Technol. 81 (1) (2015) 144–150.

[39]

Tadikamalla P.R., A look at the Burr and related distributions, Internat. Statist. Rev. 48 (3) (1980) 337–344,.

[40]

Vouk, M.A., 1992. Using reliability models during testing with non-operational profiles. In: Proceedings of the 2nd Bellcore/Purdue Workshop on Issues in Software Reliability Estimation. pp. 103–111.

[41]

Wood A., Predicting software reliability, IEEE Comput. 29 (11) (1996) 69–77,.

Digital Library

[42]

Xiao X., Okamura H., Dohi T., NHPP-based software reliability models using equilibrium distribution, IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 95 (5) (2012) 894–902,.

[43]

Yamada S., Ohba M., Osaki S., S-Shaped reliability growth modeling for software error detection, IEEE Trans. Reliab. R-32 (5) (1983) 475–484,.

[44]

Zhao, Y., Dohi, T., Okamura, H., 2018. Software Test-Run Reliability Modeling with Non-homogeneous Binomial Processes. In: 2018 IEEE 23rd Pacific Rim International Symposium on Dependable Computing. PRDC 2018, pp. 145–154. https://doi.org/10.1109/PRDC.2018.00025.

[45]

Zhao M., Xie M., On maximum likelihood estimation for a general non-homogeneous Poisson process, Scand. J. Stat. 23 (4) (1996) 597–607,.

[46]

Zimmer W.J., Keats J.B., Wang F., The Burr XII distribution in reliability analysis, J. Qual. Technol. 30 (4) (1998) 386–394,.

Cited By

Han SHan QQiao YXue KShi Z(2024)Developing Burr-XII NHPP-based software reliability growth model using Expectation Conditional Maximization AlgorithmProceedings of the 15th Asia-Pacific Symposium on Internetware10.1145/3671016.3674814(387-396)Online publication date: 24-Jul-2024
https://dl.acm.org/doi/10.1145/3671016.3674814
Yano HDohi TOkamura H(2024)Performance Comparison of Software Reliability Estimation AlgorithmsComputer10.1109/MC.2023.333416357:4(26-36)Online publication date: 3-Apr-2024
https://dl.acm.org/doi/10.1109/MC.2023.3334163

Index Terms

Burr-type NHPP-based software reliability models and their applications with two type of fault count data

Index terms have been assigned to the content through auto-classification.

Recommendations

Local polynomial software reliability models and their application
Abstract
In this paper, we propose local polynomial software reliability models (SRMs), which can be categorized into a semi-parametric modeling framework. Our models belong to the common non-homogeneous Poisson process (NHPP)-based SRMs, but possess a ...
Long-term software fault prediction with wavelet shrinkage estimation
Abstract
Wavelet shrinkage estimation received considerable attentions to estimate stochastic processes such as a non-homogeneous Poisson process in a non-parametric way, and was applied to software reliability estimation/prediction. However, it lacks the ...
Highlights
- Propose the wavelet-based long-term prediction methods for NHPP-based SRM.
- Provide a total of 2,640 prediction methods by combining different wavelet methods.
- Our wavelet prediction methods outperform parametric models in ...
An Empirical Study of Reliability Growth of Open versus Closed Source Software through Software Reliability Growth Models
APSEC '12: Proceedings of the 2012 19th Asia-Pacific Software Engineering Conference - Volume 01

The purpose of this study is to analyze the reliability growth of Open Source Software (OSS) versus software developed in-house (i.e. Closed Source Software, CSS) using Software Reliability Growth Model. This study uses 22 datasets containing failure ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Journal of Systems and Software

Journal of Systems and Software Volume 191, Issue C

Sep 2022

181 pages

ISSN:0164-1212

Issue’s Table of Contents

Elsevier Inc.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 September 2022

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 09 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Han SHan QQiao YXue KShi Z(2024)Developing Burr-XII NHPP-based software reliability growth model using Expectation Conditional Maximization AlgorithmProceedings of the 15th Asia-Pacific Symposium on Internetware10.1145/3671016.3674814(387-396)Online publication date: 24-Jul-2024
https://dl.acm.org/doi/10.1145/3671016.3674814
Yano HDohi TOkamura H(2024)Performance Comparison of Software Reliability Estimation AlgorithmsComputer10.1109/MC.2023.333416357:4(26-36)Online publication date: 3-Apr-2024
https://dl.acm.org/doi/10.1109/MC.2023.3334163

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents