SpringerLink
Log in

A Comparative Analysis on Effort Estimation for Agile and Non-agile Software Projects Using DBN-ALO

  • RESEARCH ARTICLE - SPECIAL ISSUE - INTELLIGENT COMPUTING and INTERDISCIPLINARY APPLICATIONS
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

At present, in the software industry, agile and non-agile software development approaches are followed and effort estimation is an intrinsic part of both the approaches. This work investigates the application of deep belief network (DBN) along with antlion optimization (ALO) technique for effort prediction in both agile as well as non-agile software development environment. The study also provides a prediction interval of effort to handle uncertainty in estimation. This will help the project managers to estimate the effort in ranges instead of a crisp value. The proposed DBN-ALO approach is applied on four promise repository datasets for traditional software development (non-agile), and on three agile datasets. It provides the best results in all the evaluation criteria used. The proposed approach is also statistically validated using nonparametric tests, and it is found that DBN-ALO worked best for both agile and non-agile development approaches.

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
EUR 32.99 /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 includes VAT (Germany)

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

References

  1. Rijwani, P.; Jain, S.: Enhanced software effort estimation using multi layered feed forward artificial neural network technique. Procedia Comput. Sci. 89, 307–312 (2016)

    Article  Google Scholar 

  2. Laqrichi, S.; Marmier, F.; Gourc, D.; Nevoux, J.: Integrating uncertainty in software effort estimation using bootstrap based neural networks. IFAC Pap Online 48(3), 954–959 (2015)

    Article  Google Scholar 

  3. Nassif, A.B.; Azzeh, M.; Idri, A.; Abran, A.: Software development effort estimation using regression fuzzy models. J. Comput. Intell. Neurosci. (2019). https://doi.org/10.1155/2019/8367214

    Article  Google Scholar 

  4. Zare, F.; Zare, H.K.; Fallahnezhad, M.S.: Software effort estimation based on the optimal bayesian belief network. J. Appl. Soft Comput. 49, 968–980 (2016)

    Article  Google Scholar 

  5. Sehra, S.K.; Brar, Y.S.; Kaur, N.; Sehra, S.S.: Software effort estimation using FAHP and weighted kernel LSSVM machine. J. Soft Comput. (2018). https://doi.org/10.1007/s00500-018-3639-2

    Article  Google Scholar 

  6. Kaushik, A.; Verma, S.; Singh, H.J.; Chabbra, G.: Software cost optimization integrating fuzzy system and COA-Cuckoo optimization algorithm. Int. J. Syst. Assur. Eng. Manag. 8(Suppl. 2), 1461–147 (2017)

    Google Scholar 

  7. Kaushik, A.; Tayal, D.K.; Yadav, K.; Kaur, A.: Integrating firefly algorithm in artificial neural network models for accurate software cost predictions. J. Softw. Evol. Process. 28(8), 665–688 (2016)

    Article  Google Scholar 

  8. Sivanageswara Rao, G.; Phani Krishna C.V.; Rajasekhara Rao, K.: Multi objective particle swarm optimization for software cost estimation. In: Proceedings of 48th Annual Convention of Computer Society of India-Vol I. Advances in Intelligent Systems and Computing, vol. 248, pp. 125–132 (2014)

  9. Venkataiah, V.; Mohanty, R.; Pahariya, J.S.; Nagaratna, M.: Application of ant colony optimization techniques to predict software cost estimation. Comput. Commun. Network. Internet Secur. 5, 315–325 (2017)

    Article  Google Scholar 

  10. Manifesto for Agile Software Development (2019). https://agilemanifesto.org/principles. Accessed 15 April 2019

  11. Satapathy, S.M.; Rath, S.K.: Empirical assessment of machine learning models for agile software development effort estimation using story points. J. Innov. Syst. Softw. Eng. 13(2–3), 191–200 (2017)

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. Karhunen, J.; Raiko, T.; Cho, K.H.: Unsupervised deep learning: a short review. In: Advances in Independent Component Analysis and Learning Machines, pp. 125–142. Academic Press (2015)

  14. Mirjalili, S.: The antlion optimizer. Adv. Eng. Softw. 83, 80–98 (2015)

    Article  Google Scholar 

  15. Trendowicz, A.; Jefferey, R.: Software project effort estimation. Foundations and best practice guidelines for success. Springer, Berlin (2014)

    Book  Google Scholar 

  16. Jorgensen, M.; Sjoberg, D.I.K.: An effort prediction interval approach based on the empirical distribution of previous estimation accuracy. J. Inf. Softw. Technol. 45(3), 123–136 (2003)

    Article  Google Scholar 

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

    Article  Google Scholar 

  18. Pai, D.R.; McFall, K.S.; Subramanian, G.H.: Software effort estimation using a neural network ensemble. J. Comput. Inf. Syst. 53(4), 49–58 (2013)

    Google Scholar 

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

    Article  Google Scholar 

  20. Ezghari, S.; Zahi, A.: Uncertainty management in software effort estimation using a consistent fuzzy analogy-based method. J. Appl. Soft Comput. 67, 540–557 (2018)

    Article  Google Scholar 

  21. Abdelali, Z., Hicham, M., Abdelwahed, N.: An ensemble of optimal trees for software development effort estimation. In: Smart Data and Computational Intelligence. AIT2S 2018, vol. 66, pp. 55–68. LNNS, Springer, Cham (2018)

  22. Nguyen, V.; Boehm, B.; LiGuo, H.: Determining relevant training data for effort estimation using window based COCOMO calibration. J. Syst. Softw. 147, 124–146 (2019)

    Article  Google Scholar 

  23. Ziauddin, S.; Tipu, S.K.; Zia, S.: An effort estimation model for agile software development. J. Adv Comput Sci Appl 2(1), 314–324 (2012)

    Google Scholar 

  24. Martínez, J.L.; Noriega, A.R.; Ramírez, R.J.; Licea, G.; Jiménez, S.: User stories complexity estimation using bayesian networks for inexperienced developers. J. Clust. Comput. 21(1), 715–728 (2018)

    Article  Google Scholar 

  25. Dragicevic, S.; Celar, S.; Turic, M.: Bayesian network model for task effort estimation in agile software development. J. Syst. Softw. 127, 109–119 (2017)

    Article  Google Scholar 

  26. Tanveer, B.: Hybrid effort estimation of changes in agile software development. In: Agile Processes in Software Engineering, and Extreme Programming, vol. 251, pp. 316–320. LNBIP, Springer, Cham (2016)

  27. Tanveer, B.; Guzmán, L.; Engel, U.M.: Effort estimation in agile software development: case study and improvement framework. J. Softw. Evol. Process. (2017). https://doi.org/10.1002/smr.1862

    Article  Google Scholar 

  28. Bilgayian, S.; Mishra, S.; Das, M.: Effort estimation in agile software development using experimental validation of neural network models. Int. J. Inf. Technol. 11(3), 569–573 (2019)

    Google Scholar 

  29. Britto, R.; Usman, M.; Mendes, E.: Effort estimation in agile global software development context. In: Large-Scale Development, Refactoring, Testing, and Estimation. XP 2014, vol. 199, pp. 182–192. LNBIP (2014)

  30. Usman, M.; Britto, R.; Damm, L.O.; Borstler, J.: Effort estimation in large scale software development: an industrial case study. J. Inf. Softw. Technol. 99, 21–40 (2018)

    Article  Google Scholar 

  31. Satapathy, S.M.; Panda, A.; Rath, S. K.: Story point approach based agile software effort estimation using various SVR kernel methods. In: Proceedings of the International Conference on Software Engineering and Knowledge Engineering, pp. 304–307 (2014)

  32. Tung, K.T.; Hanh, L.T.M.: A novel hybrid abc-pso algorithm for effort estimation of software projects using agile methodologies. J. Intell. Syst. 27(3), 489–506 (2018)

    Article  Google Scholar 

  33. Zakrani, A.; Najm, A.; Marzak, A.: Support vector regression based on grid-search method for agile software effort prediction. In: Proceedings of International Congress on Information Science and Technology, pp. 492–497 (2018)

  34. Tera promise: Data Categories http://openscience.us/repo/(2019) . Accessed 15 March 2019

  35. Kaur, P.; Gossain, A.: FF-SMOTE: a metaheuristic approach to combat class imbalance in binary classification. J. Appl. Artif. Intell. 33(5), 420–439 (2019)

    Article  Google Scholar 

  36. Kocaguneli, E.; Menzies, T.: Software effort models should be assessed via leave-one-out validation. J. Syst. Softw. 86(7), 1879–1890 (2013)

    Article  Google Scholar 

  37. Mittas, N.; Papatheocharous, E.; Angelis, L.; Andreou, A.S.: Integrating non-parametric models with linear components for producing software cost estimations. J. Syst. Softw. 99, 120–134 (2015)

    Article  Google Scholar 

  38. Foss, T.; Stensrud, E.; Kitchenham, B.; Myrtveit, I.: A simulation study of the model evaluation criterion MMRE. IEEE Trans. Softw. Eng. 29(11), 985–995 (2003)

    Article  Google Scholar 

  39. Kaushik, A.; Soni, A.K.; Soni, R.: An improved functional link artificial neural networks with intuitionistic fuzzy clustering for software cost estimation. Int. J. Syst. Assur. Eng. Manag. 7(1), 50–61 (2016)

    Article  Google Scholar 

  40. Shepperd, M.; MacDonell, S.: Evaluating prediction systems in software project estimation. Inf. Softw. Technol. 54, 820–827 (2012)

    Article  Google Scholar 

  41. Benala, T.R.; Korada, C.; Mall, R.; Dehuri, S.: A particle swarm optimized functional link artificial neural networks (PSO-FLANN) in software cost estimation. In: Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA). Advances in Intelligent Systems and Computing, vol. 199, pp. 59–66 (2013)

  42. Alcalá-Fdez, J.; Fernández, A.; Luengo, J.; Derrac, J.; García, S.; Sánchez, L.; Herrera, F.: KEEL data-mining software tool: dataset repository, integration of algorithms and experimental analysis framework. J. Mult. Valued Logic Soft Comput. 17(2), 255–287 (2011)

    Google Scholar 

  43. Hodges, J.L.; Lehmann, E.L.: Rank methods for combination of independent experiments in analysis of variance. Ann. Math. Stat. 33(2), 482–497 (1962)

    Article  MathSciNet  Google Scholar 

  44. Holm, S.: A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6(2), 65–70 (1979)

    MathSciNet  MATH  Google Scholar 

  45. Wilcoxon, F.: Individual comparisons by ranking methods. Biom. Bull. 1(6), 80–83 (1945)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of IT, Maharaja Surajmal Institute of Technology, New Delhi, India

    Anupama Kaushik

  2. IGDTUW (Indira Gandhi Delhi Technical University for Women), Delhi, India

    Anupama Kaushik

  3. Department of Computer Science, IGDTUW (Indira Gandhi Delhi Technical University for Women), Delhi, India

    Devendra Kr. Tayal

  4. Department of IT, IGDTUW (Indira Gandhi Delhi Technical University for Women), Delhi, India

    Kalpana Yadav

Authors
  1. Anupama Kaushik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Devendra Kr. Tayal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kalpana Yadav
    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., Tayal, D.K. & Yadav, K. A Comparative Analysis on Effort Estimation for Agile and Non-agile Software Projects Using DBN-ALO. Arab J Sci Eng 45, 2605–2618 (2020). https://doi.org/10.1007/s13369-019-04250-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-019-04250-6

Keywords

Search

Navigation