Skip to main content
Log in

RETRACTED ARTICLE: Enrichment of accurate software effort estimation using fuzzy-based function point analysis in business data analytics

  • S.I. : Emerging Intelligent Algorithms for Edge-of-Things Computing
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

This article was retracted on 14 December 2022

This article has been updated

Abstract

Accurate effort estimation is a significant task in software development, which is helpful in the scheduling and tracking of the project. A number of estimation models are available for effort calculation. However, a lot of newer models are still being proposed to obtain more accurate estimation. This paper attempts to propose a hybrid technique which incorporates both quality factors and fuzzy-based technique in function point analysis. Fuzzy logic has the capability of tackling the uncertainty issues in the estimation. The goal of this paper is to evaluate the accuracy of fuzzy analysis for software effort estimation. In this approach, fuzzy logic is used to control the uncertainty in the software size with the help of a triangular fuzzy set, and defuzzification through the weighted average method. The experimentation is done with different project data on the proposed model, and the results are tabulated. The measured effort of the proposed model is compared with that of the existing model, and finally, the performance evaluation is done based on parameters in terms of MMRE and VAF.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Change history

References

  1. Kumar MS, Rajan BC (2014) Impact of performance metrics in software effort estimation using function point analysis. Inf Int Interdiscip J 2014:2255–2268

    Google Scholar 

  2. Huang S-J et al (2006) Fuzzy decision tree approach for embedding risk assessment information into software cost estimation model. J Inf Sci Eng 22:297–313

    Google Scholar 

  3. Sharma V, Verma HK (2010) Optimized fuzzy logic based framework for effort estimation in software development. Int J Comput Sci 7(2):30–38

    Google Scholar 

  4. Huang X, Ho D, Ren J (2008) A soft computing framework for software effort estimation. Soft Comput J 10(2):23–41

    Google Scholar 

  5. Mittal H, Bhatia P (2012) A comparative study of conventional effort estimation and fuzzy effort estimation based on triangular fuzzy numbers. Int J Comput Sci Secur 10(2):36–47

    Google Scholar 

  6. Bhatnagar R et al (2010) A proposed novel framework for early effort estimation using fuzzy logic techniques. Glob J Comput Sci Technol 1(4):66–71

    Google Scholar 

  7. Lima O, Farias P (2002) A fuzzy model for function point analysis to development and enhancement project assessments. CLEI Electron J 5(2):35–42

    Google Scholar 

  8. Attarzadeh I, Hockow S (2010) Improving the accuracy of software cost estimation model based on a new fuzzy logic model. World Appl Sci J 8(2):177–184

    Google Scholar 

  9. MacDanell SR, Gray AR (1997) A comparison of modeling techniques for software development effort prediction. In: International conference on neural information processing and intelligent information systems, pp 869–872

  10. Aver M, Biffi S (2004) Increasing the accuracy and reliability of analogy-based cost estimation with extensive project feature dimension weighting. In: International symposium on empirical software engineering, pp 2165–2170

  11. Juneja S, Rana P (2013) Fuzzification of complexity matrix to calculate function points. Int J Adv Res Comput Sci Softw Eng 4(4):219–225

    Google Scholar 

  12. Mittal H, Bhatia P (2007) Optimization criterion for effort estimation using fuzzy technique. CLEI Electron J 10:20–34

    Article  Google Scholar 

  13. Ahamed F, Bouckkif S, Serhani A, Khalil I (2008) Integrating function point project information for improving the accuracy of effort estimation. In: International conference on advanced engineering computing and applications in science, pp 193–198

  14. Xia W et al (2008) A new calibration for function point complexity weights. Inf Softw Technol 50(7–8):670–683

    Article  Google Scholar 

  15. Braz MR, Virgilio SR (2006) Software effort estimation based on use cases. In: Proceeding of the international computer software and applications conference, vol 1, pp 221–228

    Google Scholar 

  16. Pantoni RP, Mossin EA, Brandao D (2008) Task effort fuzzy estimator for software development. INFOCOMP J Comput Sci 7(2):84–89

    Google Scholar 

  17. Suharjito, Nanda S, Soewito B (2016) Modeling software effort estimation using hybrid PSO-ANFIS, Intelligent Technology and Its Applications (ISITIA) in IEEE. https://doi.org/10.1109/ISITIA.2016.7828661

  18. Hari CHVMK, Prasad Reddy PVGD (2011) A fine parameter tuning for COCOMO 81 software effort estimation using particle swarm optimization. J Software Eng 5:38–48

    Article  Google Scholar 

  19. Manoj VVR, Swarup Kumar JNV (2012) A novel interval type-2 fuzzy software effort estimation using Takogi–Sugeno fuzzy controller. Int J Mod Eng Res 2(2):3245–3247

    Google Scholar 

  20. Frank Vijay J, Manokaran C (2009) Initial hybrid method for analyzing software estimation, benchmarking and risk assessment using design of software. J Comput Sci 10(5):30–38

    Google Scholar 

  21. Kumar MS, Rajan BC (2015) An accurate FFPA-PSR estimator algorithm and tool for software effort estimation. Sci World J 15:6

    Google Scholar 

  22. Krishna AB, Krishna TKR (2012) Fuzzy and swarm intelligence for software effort estimation. Adv Inf Technol Manag 11(22):246–250

    Google Scholar 

  23. Yang B, Hu H, Jia L (2008) A study of uncertainty in software cost and its impact on optimal software release time. IEEE Trans Softw Eng 34(6):813–825

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to J. Frank Vijay.

Ethics declarations

Conflict of interest

The author declares that he has no conflict of interest.

Additional information

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s00521-022-08159-4

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.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Frank Vijay, J. RETRACTED ARTICLE: Enrichment of accurate software effort estimation using fuzzy-based function point analysis in business data analytics. Neural Comput & Applic 31, 1633–1639 (2019). https://doi.org/10.1007/s00521-018-3565-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3565-3

Keywords

Navigation