Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

A hybrid model of wavelet neural network and metaheuristic algorithm for software development effort estimation

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

Software development effort estimation (SDEE) means estimating software cost and effort at the early stage of software development. It is a difficult task as the characteristics of software to be developed are not known at the time of estimation of software effort. But this is very important for software development organizations as it ultimately leads to the project’s success. This paper proposes a non-algorithmic technique for SDEE i.e. a hybrid model of wavelet neural network (WNN) and metaheuristic algorithm. Two metaheuristic algorithms i.e. firefly algorithm and bat algorithm are used. The efficiency of WNN with integration of each of these metaheuristic algorithms is investigated. Two variants of wavelet functions—Morlet and Gaussian are used as activation functions in WNN. The proposed techniques are experimentally evaluated on PROMISE SDEE repositories. It was observed that integrating metaheuristic algorithms with WNN outperformed the results of software effort prediction in comparison to traditional WNN which is not optimized using any metaheuristic technique. The results are also statistically validated using a non-parametric statistical test using IBM SPSS tool.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Boehm BW (1981) Software engineering economics. Prentice-Hall, Englewood Cliffs

    MATH  Google Scholar 

  2. Pressman RS (1997) Software engineering: a practitioner’s approach, 4th edn. McGraw-Hill, New York

    MATH  Google Scholar 

  3. Miyazaki Y, Terakado Y, Ozaki K, Nozaki N (1994) Robust regression for developing software estimation models. J Syst Softw 27:16–35

    Article  Google Scholar 

  4. Gray AR (1999) A simulation-based comparison of empirical modelling techniques for software metric models of development effort. In: Proceedings of  sixth international conference on neural information processing, Perth, WA, Australia, pp 526–531

  5. Saadi MH, Bardsiri VK, Ziaaddini F (2015) The application of meta-heuristic algorithms to improve the performance of software development effort estimation models. Int J Appl Evol Comput 6:39–68

    Article  Google Scholar 

  6. Girotra S, Sharma K (2016) Tuning of software cost drivers using BAT algorithm. In: Proceedings of third international conference on computing for sustainable global development (INDIACom) New Delhi, India, pp 1051–1056

  7. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249

    Article  Google Scholar 

  8. Kumar KV, Ravi V, Carr M, Kiran NR (2008) Software development cost estimation using wavelet neural networks. J Syst Softw 81:1853–1867

    Article  Google Scholar 

  9. Wang G, Guo L, Duan H (2013) Wavelet neural network using multiple wavelet functions in target threat assessment. Sci World J 2013:1–7

    Google Scholar 

  10. Burgess CJ, Lefley M (2001) Can genetic programming improve software cost estimation? A comparative evaluation. Inf Softw Technol 43:863–873

    Article  Google Scholar 

  11. Shan Y, Mckay RI, Lokan CJ, Essam DL (2002) Software project effort estimation using genetic programming. In: Proceedings of international conference on communications circuits and systems and west sino expositions Chengdu, China, pp 1108–1112

  12. Jørgensen M (2004) A review of studies on expert estimation of software development effort. J Syst Softw 70:37–60

    Article  Google Scholar 

  13. Huang X, Ho D, Ren J, Capretz LF (2007) Improving the COCOMO model using a neuro-fuzzy approach. Appl Soft Comp 7(1):29–40

    Article  Google Scholar 

  14. Mittas N, Athanasiades M, Angelis L (2008) Improving analogy-based software cost estimation by a resampling method. Inf Softw Technol 50:221–230

    Article  Google Scholar 

  15. Park BJ, Pedrycz W, Oh SK (2009) A design of genetically oriented fuzzy relation neural networks (FrNNs) based on the fuzzy polynomial inference scheme. IEEE Trans Fuzzy Syst 17:1310–1323

    Article  Google Scholar 

  16. Kaushik A, Soni AK, Soni R (2013) Radial basis function network using intuitionistic fuzzy C means for software cost estimation. Int J Comput Appl Technol 47:86–95

    Article  Google Scholar 

  17. Nassif AB, Ho D, Capretz LF (2013) Towards an early software estimation using log-linear regression and a multilayer perceptron model. J Syst Softw 86:144–160

    Article  Google Scholar 

  18. Dave VS, Dutta K (2014) Neural network based models for software effort estimation: a review. Artif Intell Rev 42:295–307

    Article  Google Scholar 

  19. Panda A, Satapathy SM, Rath SR (2015) Empirical validation of neural network models for agile software effort estimation based on story points. Procedia Comput Sci 57:772–781

    Article  Google Scholar 

  20. Rijwani P, Jain S (2016) Enhanced software effort estimation using multi layered feed forward. Procedia Comput Sci 89:307–312

    Article  Google Scholar 

  21. Miandoab EE, Gharehchopogh FS (2016) A novel hybrid algorithm for software cost estimation based on cuckoo optimization and K-nearest neighbors algorithms. Eng Technol Appl Sci Res 6:1018–1022

    Article  Google Scholar 

  22. Kaushik A, Tayal DK, Yadav K, Kaur A (2016) Integrating firefly algorithm in artificial neural network models for accurate software cost predictions. J Softw Evol Process 28:665–688

    Article  Google Scholar 

  23. Benala TR, Mall R (2018) DABE: differential evolution in analogy-based software development effort estimation. Swarm Evol Comput 38:158–172

    Article  Google Scholar 

  24. Kaushik A, Verma S, Singh HJ, Chabbra G (2017) Software cost optimization integrating fuzzy system and COA-Cuckoo optimization algorithm. Int J Syst Assur Eng Manag 8:1461–1471

    Article  Google Scholar 

  25. Sarro F, Petrozziello A (2018) Linear programming as a baseline for software effort estimation. ACM Trans Softw Eng Methodol (TOSEM) 27(3):12

    Article  Google Scholar 

  26. Kaur I et al (2018) Neuro fuzzy—COCOMO II model for software cost estimation. Int J Inf Technol 10(2):181–187

    Google Scholar 

  27. Bilgaiyan S, Mishra S, Das M (2018) Effort estimation in agile software development using experimental validation of neural network models. Int J Inf Technol 11(3):569–573

    Google Scholar 

  28. Hosni M, Idri A, Abran A (2019) Evaluating filter fuzzy analogy homogenous ensembles for software development effort estimation. J Softw Evol Process 31(2):e2117

    Article  Google Scholar 

  29. Mustapha H, Abdelwahed N (2019) Investigating the use of random forest in software effort estimation. Procedia Comput Sci 148:343–352

    Article  Google Scholar 

  30. Khurshid S, Shrivastava AK, Iqbal J (2019) Effort based software reliability model with fault reduction factor, change point and imperfect debugging. Int J Inf Technol. https://doi.org/10.1007/s41870-019-00286-x

    Article  Google Scholar 

  31. http://mathworld.wolfram.com/Wavelet.html. Accessed 24 Sept 2018

  32. Amara Graps (1995) An introduction to wavelets. http://wavelets.aguasonic.com/. Accessed 15 Sept 2018

  33. Yang X-S (2010) Nature-inspired metaheuristic algorithms, 2nd edn. Luniver Press, London

    Google Scholar 

  34. Pal SK, Rai CS, Singh AP (2012) Comparative study of firefly algorithm and particle swarm optimization for noisy non-linear optimization problems. Int J Intell Syst Appl 10:50–57

    Google Scholar 

  35. Yang X-S, He X (2013) Firefly algorithm: recent advances and applications. Int J Swarm Intell 1:36–50

    Article  Google Scholar 

  36. Yang X-S (2010) Engineering optimisation: an introduction with metaheuristic applications. Wiley, New York

    Book  Google Scholar 

  37. Yang XS (2009) Firefly algorithms for multimodal optimization. In: Proceedings of 5th symposium on stochastic algorithms, foundations and applications, lecture notes in computer science, vol 5792. pp 169–178

  38. Huang SJ, Chiu NH, Liu YJ (2008) A comparative evaluation on the accuracies of software effort estimates from clustered data. Inf Softw Technol 50:879–888

    Article  Google Scholar 

  39. Foss T, Stensrud E, Kitchenham B, Myrtveit I (2003) A simulation study of the model evaluation criteria MMRE. IEEE Trans Softw Eng 29:985–995

    Article  Google Scholar 

  40. http://openscience.us/repo/. Accessed 15 Aug 2018

Download references

Author information

Authors and Affiliations

  1. Department of IT, Maharaja Surajmal Institute of Technology, C-4, Janakpuri, New Delhi, India

    Anupama Kaushik & Niyati Singal

Authors
  1. Anupama Kaushik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Niyati Singal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anupama Kaushik.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaushik, A., Singal, N. A hybrid model of wavelet neural network and metaheuristic algorithm for software development effort estimation. Int. j. inf. tecnol. 14, 1689–1698 (2022). https://doi.org/10.1007/s41870-019-00339-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-019-00339-1

Keywords

Search

Navigation