Skip to main content
SpringerLink
Log in

Software Size Estimation in Design Phase Based on MLP Neural Network

  • Conference paper
  • First Online:
Recent Advances in Information and Communication Technology 2017 (IC2IT 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 566))

Included in the following conference series:

  • 821 Accesses

Abstract

Size estimation is one of important processes related to success of software project management. This paper presents novel software size estimation model by using Multilayer Perceptron approach. Software size in terms of Lines of code is used as criterion variable. Structural complexity metrics are used as predictors. The metrics can be captured from a software design model named UML Class diagram. A high predictive ability of the model is shown with correlation coefficient measure. Moreover, four training algorithms; Levenberg-Marquardt, Scaled Conjugate Gradient, Broyden-Fletcher-Golfarb-Shanno and Bayesian Regularization, have been applied on the network for better estimation. The obtained results indicate the highest accuracy on the model with Bayesian Regularization algorithm.

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

Access this chapter

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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

Institutional subscriptions

References

  1. Bhatia, S., Malhotra, J.: A survey on impact of lines of code on software complexity. In: 2014 International Conference on Advances in Engineering and Technology Research (ICAETR - 2014), pp. 1–4, Unnao (2014)

    Google Scholar 

  2. Tashtoush, Y., Al-Maolegi, M., Arkok, B.: The correlation among software complexity metrics with case study. Int. J. Adv. Comput. Res. 4(2), 414–419 (2014)

    Google Scholar 

  3. Zhang, H.: An investigation of the relationships between lines of code and defects. In: IEEE International Conference on Software Maintenance (ICSM 2009), pp. 274–283, Edmonton (2009)

    Google Scholar 

  4. Bhati, G.K., Pooja: An approach for software size estimation. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 4(2), 19–25 (2014)

    Google Scholar 

  5. Jain, S., Yadav, V., Singh, R.: An approach for OO software size estimation using predictive object point metrics. In: 2014 International Conference on Computing for Sustainable Global Development (INDIACom), pp. 421–424 (2014)

    Google Scholar 

  6. Harizi, M.: The role of class diagram in estimating software size. Int. J. Comput. Appl. 44(5), 31–33 (2012)

    Google Scholar 

  7. Baboo, S.S., Shereef, I.K.: An efficient weather forecasting system using artificial neural network. Int. J. Environ. Sci. Dev. 1(4), 321–326 (2010)

    Article  Google Scholar 

  8. Gurusen, E., Kayakutlu, G., Daim, T.U.: Using artificial neural network models in stock market index prediction. Expert Syst. Appl. 38(8), 10389–11039 (2011)

    Article  Google Scholar 

  9. Paswan, R.P., Begum, S.A.: MLP for prediction of area and rice production of upper Brahmaputra valley zone of Assam. In: The 4th International Conference on Computing, Communications and Networking Technologies (ICCCNT), pp. 1–9 (2013)

    Google Scholar 

  10. Capilla, C.: Prediction of hourly Ozone concentrations with multiple regression and multilayer perceptron models. Int. J. Sustain. Dev. Plann. 11(4), 558–565 (2016)

    Article  Google Scholar 

  11. Aggarwal, K.K., Singh, Y., Kaur, A., Malhotra, R.: Application of artificial neural network for predicting maintainability using object-oriented metrics. Int. J. Comput. Electr. Autom. Control Inf. Eng. 2(10), 3552–3556 (2008)

    Google Scholar 

  12. Subitsha, P., Rajan, J.K.: Artificial neural network models for software effort estimation. Int. J. Technol. Enhancements Emerg. Eng. Res. 2(4), 76–80 (2014)

    Google Scholar 

  13. Li, H.F., Cheung, W.K.: An empirical study of software metrics. IEEE Trans. Softw. Eng. 13(6), 697–708 (1987)

    Article  Google Scholar 

  14. Kiewkanya, M., Surak, S.: Constructing C++ software size estimation model from class diagram. In: 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 1–6, Khon Kaen (2016)

    Google Scholar 

  15. Sola, J., Sevilla, J.: Importance of input data normalization for the application of neural networks to complex industrial problems. IEEE Trans. Nucl. Sci. 44(3), 1464–1468 (1997)

    Article  Google Scholar 

  16. Jayalakshmi, T., Santhakumaran, A.: Statistical normalization and back propagation for classification. Int. J. Comput. Theor. Eng. 3(1), 89–93 (2011)

    Article  Google Scholar 

  17. Nayak, S.C., Misra, B.B., Behera, H.S.: Impact of data normalization on stock index forecasting. Int. J. Comput. Inf. Syst. Ind. Manage. Appl. 6, 257–269 (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Science, Department of Computer Science, Chiang Mai University, Chiang Mai, Thailand

    Benjamas Panyangam & Matinee Kiewkanya

Authors
  1. Benjamas Panyangam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matinee Kiewkanya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Benjamas Panyangam .

Editor information

Editors and Affiliations

  1. Faculty of Information Technology, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand

    Phayung Meesad

  2. Faculty of Information Technology, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand

    Sunantha Sodsee

  3. Lehrgebiet Kommunikationsnetze, FernUniversität in Hagen, Hagen, Germany

    Herwig Unger

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Panyangam, B., Kiewkanya, M. (2018). Software Size Estimation in Design Phase Based on MLP Neural Network. In: Meesad, P., Sodsee, S., Unger, H. (eds) Recent Advances in Information and Communication Technology 2017. IC2IT 2017. Advances in Intelligent Systems and Computing, vol 566. Springer, Cham. https://doi.org/10.1007/978-3-319-60663-7_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-60663-7_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60662-0

  • Online ISBN: 978-3-319-60663-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation