Skip to main content
SpringerLink
Log in

A memetic approach for optimizing software effort estimation using anti-predatory NIA

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

Abstract

Effort estimation in the primary steps of software development is one of the most essential and pivotal tasks. It significantly impacts the success of the overall development of software projects. Inaccurately estimating software projects has been a persistent problem for software development organizations. The Constructive Cost Model (COCOMO) has been widely used for software effort estimation, but its existing parameters often fail to provide realistic results in the present context of development. In recent years, researchers have focused on the utilization of Nature-Inspired Algorithms (NIAs) to optimize the parameters of COCOMO to improve its performance. The necessity to increase estimation precision urged the authors to propose a novel approach Memetic Improved Anti-Predatory Nature Inspired Algorithm (MI-APNIA). The proposed MI-APNIA integrates the concept of memeplexes and a frog's defence strategy when it senses danger from a predator. The MI-APNIA algorithm improves the exploitation phase by integrating information from the global best solution into the solution search space. Leveraging the exceptional capabilities of the MI-APNIA algorithm, a coherent and reliable parametric model is established for the precise software effort estimation based on COCOMO variants, thereby showcasing improved parameter tuning compared to other existing NIAs. The results demonstrate a significant improvement in terms of Mean Magnitude Relative Error (MMRE), compared to the basic COCOMO Model with a remarkable 78.3% enhancement. Additionally, the proposed approach achieves a notable improvement in other NIA such as 10.80% better than I-APNIA, 10.96% better than APNIA, 16.5% better than SFLA, 10.8% better than PSO, and 82.19% better than GA in COCOMO V3 Variant.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Ramessur MA, Nagowah SD (2021) A predictive model to estimate effort in a sprint using machine learning techniques. Int J Inf Technol 13:1101–1110. https://doi.org/10.1007/s41870-021-00669-z

    Article  Google Scholar 

  2. Jorgensen M, Shepperd M (2006) A systematic review of software development cost estimation studies. IEEE Trans Softw Eng 33:33–53. https://doi.org/10.1109/TSE.2007.256943

    Article  Google Scholar 

  3. Padmaja M, Haritha D (2017) Software effort estimation using meta heuristic algorithm. Int J Adv Res Comput Sci. https://doi.org/10.5815/ijitcs.2017.05.07

    Article  Google Scholar 

  4. Khan MS, Jabeen F et al (2021) Metaheuristic algorithms in optimizing deep neural network model for software effort estimation. IEEE Access 9:60309–60327. https://doi.org/10.1109/ACCESS.2021.3072380

    Article  Google Scholar 

  5. Shepperd M, Schofield C, Kitchenham B (1996) Effort estimation using analogy. Proc IEEE Int Conf Softw Eng. https://doi.org/10.1109/ICSE.1996.493413

    Article  Google Scholar 

  6. Tausworthe RC (1979) The work breakdown structure in software project management. J Syst Softw 1:181–186. https://doi.org/10.1016/0164-1212(79)90018-9

    Article  Google Scholar 

  7. Huang X, Ho D, Ren J, Capretz LF (2007) Improving the COCOMO model using a neuro-fuzzy approach. Appl Soft Comput 7:29–40. https://doi.org/10.1016/j.asoc.2005.06.007

    Article  CAS  Google Scholar 

  8. Chhabra S, Singh H (2020) Optimizing design parameters of fuzzy model based cocomo using genetic algorithms. Int J Inf Technol 12:1259–1269. https://doi.org/10.1007/s41870-019-00325-7

    Article  Google Scholar 

  9. Dave VS, Dutta K (2014) Neural network based models for software effort estimation: a review. Artif Intell Rev 42:295–307. https://doi.org/10.1007/s10462-012-9339-x

    Article  Google Scholar 

  10. Nassif AB, Azzeh M et al (2016) Neural network models for software development effort estimation: a comparative study. Neural Comput Appl 27:2369–2381. https://doi.org/10.1007/s00521-015-2127-1

    Article  Google Scholar 

  11. Bilgaiyan S, Mishra S, Das M (2019) Effort estimation in agile software development using experimental validation of neural network models. Int J Inf Technol 11:569–573. https://doi.org/10.1007/s41870-018-0131-2

    Article  Google Scholar 

  12. Goyal S, Parashar A (2018) Machine learning application to improve COCOMO model using neural networks. Int J Inf Technol Comput Sci (IJITCS) 3:35–51. https://doi.org/10.5815/ijitcs.2018.03.05

    Article  Google Scholar 

  13. Zima K (2015) The case-based reasoning model of cost estimation at the preliminary stage of a construction project. Proced Eng 122:57–64. https://doi.org/10.1016/j.proeng.2015.10.007

    Article  Google Scholar 

  14. Putnam LH (1992) Myers. Measures for Excellence Yourdon Press Computing Series

  15. Matson JE, Barrett BE, Mellichamp JM (1994) Software development cost estimation using function points. IEEE Trans Softw Eng 20:275–287

    Article  Google Scholar 

  16. Barry B (1981) Software engineering economics. IEEE Trans Softw Eng 197:40

    Google Scholar 

  17. Kaur I, Narula GS, Wason R, Jain V, Baliyan A (2018) Neuro fuzzy—COCOMO II model for software cost estimation. Int J Inf Technol 10:181–187. https://doi.org/10.1007/s41870-018-0083-6

    Article  Google Scholar 

  18. Marco R, Suryana N et al (2019) A systematic literature review on methods for software effort estimation. J Theor Appl Inf Technol 97:434–464

    Google Scholar 

  19. Rajakumar R, Dhavachelvan P, Vengattaraman T (2016) A survey on nature inspired meta-heuristic algorithms with its domain specifications. Int Conf Commun Electron Syst (ICCES). https://doi.org/10.1109/CESYS.2016.7889811

    Article  Google Scholar 

  20. Sehra SK, Brar YS, Kaur N, Sehra SS (2017) Research patterns and trends in software effort estimation. Inf Softw Technol 91:1–21. https://doi.org/10.1016/j.infsof.2017.06.002

    Article  Google Scholar 

  21. Chalotra S, Sehra SK, Brar YS, Kaur N (2015) Tuning of cocomo model parameters by using bee colony optimization. Indian J Sci Technol 8:1. https://doi.org/10.17485/ijst/2015/v8i14/70010

    Article  Google Scholar 

  22. Price KV (2013) Differential evolution InHandbook of optimization: from classical to modern approach, pp 187–214

  23. Sachan RK, Nigam A et al (2016) Optimizing basic COCOMO model using simplified genetic algorithm. Proced Comput Sci 89:492–498. https://doi.org/10.1016/j.procs.2016.06.107

    Article  Google Scholar 

  24. Ghatasheh N, Faris H et al (2015) Optimizing software effort estimation models using firefly algorithm. J Softw Eng Appl 8:133–142. https://doi.org/10.4236/jsea.2015.83014

    Article  Google Scholar 

  25. Sabbagh Jafari SM, Ziaaddini F (2016) Higher education complex of bam and institute of electrical and electronics engineers. In: 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC2016) : proceedings : 9–11 March 2016

  26. Langsari K, Sarno R, Sholiq S (2018) Optimizing effort parameter of COCOMO II using particle swarm optimization method. TELKOMNIKA (Telecommun Comput Electron Control) 16:2208–2216. https://doi.org/10.12928/TELKOMNIKA.v16i5.9703

    Article  Google Scholar 

  27. KhatibiBardsiri V, Jawawi DN, Hashim SZ, Khatibi E (2013) A PSO-based model to increase the accuracy of software development effort estimation. Softw Qual J 21:501–526. https://doi.org/10.1007/s11219-012-9183-x

    Article  Google Scholar 

  28. Maleki I, Ghaffari A, Masdari M (2014) A new approach for software cost estimation with hybrid genetic algorithm and ant colony optimization. Int J Innov Appl Stud 5:72–81

    Google Scholar 

  29. Ahmad SW, Bamnote GR (2019) Whale–crow optimization (WCO)-based optimal regression model for software cost estimation. Sādhanā 44:1–5. https://doi.org/10.1007/s12046-019-1085-1S

    Article  MathSciNet  CAS  Google Scholar 

  30. Nandal D, Sangwan OP (2018) Software cost estimation by optimizing COCOMO model using hybrid BATGSA algorithm. Int J Intell Eng Syst 11:250–263. https://doi.org/10.22266/ijies2018.0831.25

    Article  Google Scholar 

  31. Kaushik A, Singal N (2019) A hybrid model of wavelet neural network and metaheuristic algorithm for software development effort estimation. Int J Inf Technol. https://doi.org/10.1007/s41870-019-00339-1

    Article  Google Scholar 

  32. Wang L, Fang C (2011) An effective shuffled frog-leaping algorithm for multi-mode resource-constrained project scheduling problem. Inf Sci 181:4804–4822. https://doi.org/10.1016/j.ins.2011.06.014

    Article  MathSciNet  Google Scholar 

  33. Amiri B, Fathian M, Maroosi A (2009) Application of shuffled frog-leaping algorithm on clustering. Int J Adv Manuf Technol 45:199–209. https://doi.org/10.1007/s00170-009-1958-2

    Article  Google Scholar 

  34. Adam SP, Alexandropoulos SA, Pardalos PM, Vrahatis MN (2019) No free lunch theorem: a review. Approximation and optimization: Algorithms, complexity and applications, pp 57–82

  35. Sachan RK, Kushwaha DS (2019) A generalized and robust anti-predatory nature-inspired algorithm for complex problems. Int J Appl Metaheuristic Comput (IJAMC) 10:75–91. https://doi.org/10.4018/IJAMC.2019010105

    Article  Google Scholar 

  36. Sachan RK, Kushwaha DS (2020) Anti-predatory NIA for unconstrained mathematical optimization problems. Int J Swarm Intell Res (IJSIR) 11:1–23. https://doi.org/10.4018/IJSIR.2020010101

    Article  Google Scholar 

  37. Sharma A, Rajpoot DS (2022) A frog based nature inspired algorithm for solving optimization problem. Int Conf Inform (ICI). https://doi.org/10.1109/ICI53355.2022.9786875

    Article  Google Scholar 

  38. Bragg AN (1945) Notes on the psychology of frogs and toads. J Gen Psychol 32:27–37. https://doi.org/10.1080/00221309.1945.10544481

    Article  Google Scholar 

  39. Borade JG, Khalkar VR (2013) Software project effort and cost estimation techniques. Int J Adv Res Comput Sci Softw Eng. https://www.researchgate.net/publication/313243865

  40. Sheta AF (2006) Estimation of the COCOMO model parameters using genetic algorithms for NASA software projects. J Comput Sci 2:118–123

    Article  Google Scholar 

  41. Bailey JW, Basili VR (1981) A meta-model for software development resource expenditures. In Proceedings of the 5th international conference on Software engineering, pp 107–116

Download references

Author information

Authors and Affiliations

  1. Department of CSE & IT, Jaypee Institute of Information Technology, Noida, India

    Archana Sharma & Dharmveer Singh Rajpoot

Authors
  1. Archana Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dharmveer Singh Rajpoot
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Archana Sharma.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharma, A., Rajpoot, D.S. A memetic approach for optimizing software effort estimation using anti-predatory NIA. Int. j. inf. tecnol. 16, 641–649 (2024). https://doi.org/10.1007/s41870-023-01652-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-023-01652-6

Keywords

Search

Navigation