Skip to main content
SpringerLink
Log in
Book cover

International Conference on Swarm, Evolutionary, and Memetic Computing

SEMCCO 2014: Swarm, Evolutionary, and Memetic Computing pp 765–776Cite as

An Efficient Method for Parameter Estimation of Software Reliability Growth Model Using Artificial Bee Colony Optimization

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8947))

Abstract

One of the established trends of research areas and practices in software engineering that dealt with the measurement and enhancement of reliability is software reliability engineering. Stochastic software reliability models find typical usage for analysis. These are the models that perform modeling on failure process of the software and exploit other software metrics or failure data as cornerstone for parameter estimation. The ability of the models for estimating and predicting the current reliability and future failure behavior, respectively, is high. Due to any failures and faults in the system, a product becomes unreliable. The lack of understanding of nature of the software makes the measurement of software reliability as a challenging task. It is not possible to determine a best way to measure the reliability and other aspects of software. This paper proposes an efficient software reliability growth model (SRGM) in which logistic exponential TEF is exploited. This model offers increased failure rate recognition and suitable ways to resolve faults and so on. Our work estimates the SRGM parameters using optimization algorithm. Such estimation can aid in developing precise software reliability model. In order to accomplish the optimization, we use artificial bee colony (ABC) algorithm. As the parameters optimization considerably improves the quality of parameters to be used for reliability growth model, reliability growth can also be improved considerably.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Gokhale, S.S., Lyu, M.R., Trivedi, K.S.: software reliability analysis incorporating fault detection and debugging activities. In: Proceedings of the Ninth International Symposium on Software Reliability Engineering, Paderborn, Germany, pp. 202–211, November 1998

    Google Scholar 

  2. Goseva-Popstojanova, K., Trivedi, K.S.: Failure correlation in software reliability models. IEEE Trans. Reliab. 49(1), 37–48 (2000)

    Article  MATH  Google Scholar 

  3. Gokhale, S.S., Lyu, M.R., Trivedi, K.S.: Incorporating fault debugging activities into software reliabilitymodels: a simulation approach. IEEE Trans. Reliab. 55(2), 281–292 (2006)

    Article  Google Scholar 

  4. Kim, M.C., Jang, S.C., Ha, J.J.: Possibilities and limitations of applying software reliability growth models to safetycritical software. J. Nucl. Eng. Technol. 39(2), 145–148 (2007)

    MATH  Google Scholar 

  5. Huang, C.-Y., Kuo, S.-Y., Lyu, M.R.: An assessment of testing-effort dependent software reliability growth models. IEEE Trans. Reliab. 56(2), 198–211 (2007)

    Article  MATH  Google Scholar 

  6. Andersson, C.: A replicated empirical study of a selection method for software reliability growth models. J. Empir. Softw. Eng. 12(2), 161–182 (2007)

    Article  Google Scholar 

  7. Williams, D.R.P.: Study of the warranty cost model for software reliability with an imperfect debugging phenomenon. Turk. J. Electr. Eng. 15(3), 369–381 (2007)

    Google Scholar 

  8. Utkin, L.V., Zatenko, S.I., Coolen, F.P.A.: Combining imprecise Bayesian and maximum likelihood estimation for reliability growth models. In: Proceedings of the Sixth International Symposium on Imprecise Probability: Theories and Applications, Durham, UK (2009)

    Google Scholar 

  9. Son, H.S., Kang, H.G., Chang, S.C.: Procedure for application of software reliability growth models to NPP PSA. J. Nucl. Eng. Technol. 41(8), 1065–1072 (2009)

    Article  Google Scholar 

  10. Bose, D., Kundu, S., Biswas, S., Das, S.: Circular antenna array design using novel perturbation based artificial bee colony algorithm. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds.) SEMCCO 2012. LNCS, vol. 7677, pp. 459–466. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  11. Quadri, S.M.K., Ahmad, N.: Software reliability growth modeling with new modified weibull testing–effort and optimal release policy. Int. J. Comput. Appl. 6(12), 1–10 (2010)

    Google Scholar 

  12. Bosea, D., Biswasa, S., Vasilakosb, A.V., Lahaa, S.: Optimal filter design using an improved artificial bee colony algorithm. Inf. Sci. 281, 443–461 (2014)

    Article  MATH  Google Scholar 

  13. Mir, K.A.: A software reliability growth model. J. Mod. Math. Stat. 5(1), 13–16 (2011)

    Article  Google Scholar 

  14. Quadri, S.M.K., Ahmad, N., Farooq, S.U.: Software reliability growth modeling with generalized exponential testing - effort and optimal software release policy. Glob. J. Comput. Sci. Technol. 11(2), 27–42 (2011)

    Google Scholar 

  15. Inoue, S., Yamada, S.: A bivariate software reliability model with change-point and its applications. Am. J. Oper. Res. 1(1), 1–7 (2011)

    Article  Google Scholar 

  16. Ahmad, N., Quadri, S.M.K., Mohd, R.: Comparison of predictive capability of software reliability growth models with exponentiated weibull distribution. Int. J. Comput. Appl. 15(6), 40–43 (2011)

    Google Scholar 

  17. Prasad, R.S., Rao, K.R.H., Kantha, R.R.L.: Software reliability measuring using modified maximum likelihood estimation and SPC. Int. J. Comput. Appl. 21(7), 1–5 (2011)

    Google Scholar 

  18. Joseph, S., Shouri, P.V., Raj, J.V.P.: A simplified model for evaluating software reliability at the developmental stage. Int. J. Softw. Eng. (IJSE) 1(5), 125–131 (2011)

    Google Scholar 

  19. Karaboga, D., Akay, B.: a comparative study of artificial bee colony algorithm. J. Appl. Math. Comput. 214, 108–132 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  20. Karaboga, D., Ozturk, C.: Fuzzy clustering with artificial bee colony algorithm. J. Sci. Res. Essays 5(14), 1899–1902 (2010)

    Google Scholar 

  21. Karaboga, D., Gorkemli, B., Ozturk, C., Karaboga, N.A.: Comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif. Intell. Rev. 42(1), 21–57 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of IT, GITAM University, Visakhapatnam, Andhra Pradesh, India

    Rao K. Mallikharjuna

  2. School of Computing, GRIET, Hyderabad, India

    Anuradha Kodali

Authors
  1. Rao K. Mallikharjuna
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anuradha Kodali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rao K. Mallikharjuna .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, IIT, New Delhi, India

    Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India

    Swagatam Das

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Mallikharjuna, R.K., Kodali, A. (2015). An Efficient Method for Parameter Estimation of Software Reliability Growth Model Using Artificial Bee Colony Optimization. In: Panigrahi, B., Suganthan, P., Das, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science(), vol 8947. Springer, Cham. https://doi.org/10.1007/978-3-319-20294-5_65

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-20294-5_65

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20293-8

  • Online ISBN: 978-3-319-20294-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation