Skip to main content
SpringerLink
Log in

Fuzzy Cognitive Mapping Analysis to Recommend Machine Learning-Based Effort Estimation Technique for Web Applications

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

Effort estimation is a fairly researched field in the area of software engineering. Algorithmic and non-algorithmic methods are the two popular ways of estimating software development efforts. Various machine learning techniques are also being used to determine project efforts based on the historical project-related dataset. These techniques consume an array of project characteristics to estimate the project cost. The selection of the right technique to correctly determine the project cost is a significant challenge that the software industry is facing. This paper presents a fuzzy cognitive mapping (FCM) approach to recommend the best machine learning-based software estimation technique for Web applications. FCM shows synergistic interactions between system variables, and this property is used in the context of Web application estimation for suggesting an estimation technique based on the Web project configuration. To counter the ambiguity in defining abstract relationships between system variables, this article also proposes to incorporate fuzzy numbers. The current analysis involves using five different estimation techniques on 125 student project records. The mean square error (MSE) was taken as a performance metric to declare the supremacy of one estimation technique over others. The experimental results show that the selection of an effort estimation technique should not ignore the presence of project characteristics in the input vector. The achievement of this work is that the proposed technique is capable of recommending the suitable most Web estimation model based on project credentials for a specific Web project; it refrains from suggesting an estimation model optimum for the most project configurations. The FCM approach on software estimation technique recommendation results in a probability of success equals to 70%.

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Wen, J., Li, S., Lin, Z., Hu, Y., Huang, C.: Systematic literature review of machine learning based software development effort estimation models. Inf. Softw. Technol. 54(1), 41–59 (2012)

    Google Scholar 

  2. Ozesmi, U.: Conservation strategies for sustainable resource use in the Kizilirmak Delta in Turkey (1999)

  3. Boehm, B.: Software Engineering Economics. Prentice-Hall, NJ (1981)

    MATH  Google Scholar 

  4. Heemstra, F.: Software cost estimation. Inf. Softw. Technol. 34, 627–639 (1992)

    Google Scholar 

  5. Hiihn, J., Habib-Agahi, H.: Cost estimation of software intensive projects: a survey of current practices. In: Proceedings of the International Conference on Software Engineering, pp. 276–287 (1991)

  6. Silhavy, P., Silhavy, R., Prokopova, Z.: Categorical variable segmentation model for software development effort estimation. IEEE Access 7, 9618–9626 (2019)

    Google Scholar 

  7. Rajput, G.S., Litoriya, R.: Corad agile method for agile software cost estimation. OALib 01(03), 1–13 (2014)

    Google Scholar 

  8. I. F. P. U. Group: Function point counting practices manual. USA, 4.2 (2004)

  9. Boehm, B., Ray, M., Steece, B.: Software cost estimation with COCOMO II. Prentice Hall PTR, Upper Saddle River (2000)

    Google Scholar 

  10. Putnam, L.H.: A general empirical solution to the macro software sizing and estimating problem. IEEE Trans. Softw. Eng. 4(4), 345–361 (1978)

    MATH  Google Scholar 

  11. Winter, M.: Predictive power for price-to-win. https://www.pricesystems.com/price-to-win/ (2019). Accessed 01 Aug 2019

  12. Pillai, K., Nair, S.: A model for software development effort and cost estimation. IEEE Trans. Softw. Eng. 23(8), 485–497 (1997)

    Google Scholar 

  13. Huang, S.-J., Chiu, N.-H.: Applying fuzzy neural network to estimate software development effort. Appl. Intell. 30(2), 73–83 (2009)

    Google Scholar 

  14. Prateek, P., Ratnesh, L.: Securing and authenticating healthcare records through blockchain technology. Cryptologia 0(0), 1–16 (2020)

  15. Pandey, P., Litoriya, R.: Securing E-health networks from counterfeit medicine penetration using Blockchain. Wirel. Pers. Commun. (2020). https://doi.org/10.1007/s11277-020-07041-7

    Article  Google Scholar 

  16. Pandey, P., Litoriya, R.: Implementing healthcare services on a large scale: challenges and remedies based on blockchain technology. Health Policy Technol (2020). https://doi.org/10.1016/j.hlpt.2020.01.004

    Article  Google Scholar 

  17. Kabra, N., Bhattacharya, P., Tanwar, S., Tyagi, S.: MudraChain: blockchain-based framework for automated cheque clearance in financial institutions. Future Gen. Comput. Syst. 102, 574–587 (2020)

    Google Scholar 

  18. Hughes, R.: Expert judgment as an estimating method. Inf. Softw. Technol. 38(2), 67–75 (1996)

    Google Scholar 

  19. Dalkey, N., Helmer, O.: An experimental application of delphi method to the use of experts. Manage. Sci. 9(3), 458–467 (1963)

    Google Scholar 

  20. Rgensen, M.: Forecasting of software development work effort: evidence on expert judgement and formal models. Int. J. Forecast. 23(3), 449–462 (2007)

    Google Scholar 

  21. Reifer, D.: Web-development estimating quick-time-to-market software. IEEE Softw. 17(8), 57–64 (2000)

    Google Scholar 

  22. Pressman, R.: Software Engineering: A Practitioner’s Approach, 7th edn. McGrawHll, New York (2010)

    MATH  Google Scholar 

  23. Mendes, E., Mosley, N., Counsell, S.: Early Web size measures and effort prediction for Web costimation. In: Proceedings. 5th International Workshop on Enterprise Networking and Computing in Healthcare Industry (IEEE Cat. No.03EX717), pp. 18–39 (2003)

  24. Valipour, F., Valipour, S.: Cost estimation for web software applications use case point, language, size and complexity factors (2018)

  25. Ruhe, M., Jeffery, R., Wieczorek, I.: Cost estimation for web applications. In: 25th International Conference on Software Engineering, 2003. Proceedings, vol. 6, pp. 285–294 (2004)

  26. Litoriya, R., Kothari, A.: Cost estimation of web projects in context with agile paradigm: improvements and validation. Int. J. Softw. Eng. 6(2), 91–114 (2013)

    Google Scholar 

  27. Litoriya, R., Kothari, A.: An efficient approach for agile web based project estimation: AgileMOW. J. Softw. Eng. Appl. 06(06), 297–303 (2013)

    Google Scholar 

  28. Pandey, P., Litoriya, R.: Fuzzy AHP based identification model for efficient application development. J. Intell. Fuzzy Syst. (2019). https://doi.org/10.3233/JIFS-190508

    Article  Google Scholar 

  29. Mendes, E.: A comparison of techniques for web effort estimation. In: Proceedings—1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007, pp. 334–343 (2007)

  30. Corazza, A., Di Martino, S., Ferrucci, F., Gravino, C., Sarro, F., Mendes, E.: Using tabu search to configure support vector regression for effort estimation. Empir. Softw. Eng. 18(3), 506–546 (2013)

    Google Scholar 

  31. Idri, A., Elyassami, S.: Applying fuzzy ID3 decision tree for software effort estimation. 8(4), 131–138 (2011)

  32. Corona, E., Concas, G., Marchesi, M., Barabino, G., Grechi, D.: Effort estimation of web applications through web CMF objects. In: 2012 Joint Conference of the 22nd International Workshop on Software Measurement and the 2012 Seventh International Conference on Software Process and Product Measurement, pp. 15–22 (2012)

  33. Pandey, M., Litoriya, R., Pandey, P.: An ISM approach for modeling the issues and factors of mobile app development. Int. J. Softw. Eng. Knowl. Eng. 28(07), 937–953 (2018)

    Google Scholar 

  34. Pandey, M., Litoriya, R., Pandey, P.: Identifying causal relationships in mobile app issues: an interval type-2 fuzzy DEMATEL approach. Wireless Pers. Commun. 108, 683–710 (2019)

    Google Scholar 

  35. Pandey, M., Litoriya, R., Pandey, P.: Application of fuzzy DEMATEL approach in analyzing mobile app issues. Program. Comput. Softw. 45(5), 268–287 (2019)

    Google Scholar 

  36. Pandey, M., Ratnesh, L., Pandey, P.: Validation of existing software effort estimation techniques in context with mobile software applications. Wireless Pers. Commun. (2019). https://doi.org/10.1007/s11277-019-06805-0

    Article  Google Scholar 

  37. Pandey, M., Litoriya, R., Pandey, P.: Novel approach for mobile based app development incorporating MAAF. Wireless Pers. Commun. 107(4), 1687–1708 (2019)

    Google Scholar 

  38. Pandey, M., Litoriya, R., Pandey, P.: Mobile App development based on agility function. Ingénierie des systèmes d’information RSTI série ISI 23(6), 19–44 (2018)

    Google Scholar 

  39. Pandey, M., Litoriya, R., Pandey, P.: Perception-based classification of mobile apps: a critical review. In: Luhach, A.K., Hawari, K.B.G., Mihai, I.C., Hsiung, P.-A., Mishra, R.B. (eds.) Smart computational strategies: theoretical and practical aspects, pp. 121–133. Springer Singapore, Singapore (2019)

    Google Scholar 

  40. Kosko, B.: Fuzzy cognitive maps. Int. J. Man Mach. Stud. 24(1), 65–75 (1986)

    MATH  Google Scholar 

  41. Lee, I.K., Kim, H.S., Cho, H.: Design of activation functions for inference of fuzzy cognitive maps: application to clinical decision making in diagnosis of pulmonary infection. Healthc. Inform. Res. 18(2), 105 (2012)

    Google Scholar 

  42. Douali, N., Papageorgiou, E.I., Roo, J.D., Cools, H., Jaulent, M.C.: Clinical decision support system based on fuzzy cognitive maps. J. Comput. Sci. Syst. Biol. 8, 112–120 (2015). https://doi.org/10.4172/jcsb.1000177

    Article  Google Scholar 

  43. Amirkhani, A., Papageorgiou, E.I., Mosavi, M.R., Mohammadi, K.: A novel medical decision support system based on fuzzy cognitive maps enhanced by intuitive and learning capabilities for modeling uncertainty. Appl. Math. Comput. 337, 562–582 (2018)

    Google Scholar 

  44. Guo, K., et al.: A hybrid fuzzy cognitive map/support vector machine approach for EEG-based emotion classification using compressed sensing. Int. J. Fuzzy Syst. 21(1), 263–273 (2019)

    Google Scholar 

  45. Jenitha, G., Ezhil, S., Kumaravel, A.: Learning methodology for effective teaching in fuzzy cognitive maps (FCM). Indian J. Comput. Sci. Eng. 8(6), 714–718 (2017)

    Google Scholar 

  46. Mago, V.K., et al.: Analyzing the impact of social factors on homelessness: a Fuzzy Cognitive Map approach. BMC Med. Inform. Decis. Mak. 13(1), 94 (2013)

    Google Scholar 

  47. Khakzad, H.: Application of fuzzy cognitive map-based TRIZ inventive principles for sustainable sediment management in dam reservoirs. H2Open J 2(1), 137–145 (2019)

    Google Scholar 

  48. Beena, P., Ganguli, R.: Structural damage detection using fuzzy cognitive maps and Hebbian learning. Appl. Soft Comput. 11(1), 1014–1020 (2011)

    Google Scholar 

  49. Bağdatlı, M.E.C., Akbıyıklı, R., Papageorgiou, E.I.: A fuzzy cognitive map approach applied in cost-benefit analysis for highway projects. Int. J. Fuzzy Syst. 19(5), 1512–1527 (2017)

    Google Scholar 

  50. Xirogiannis, G., Glykas, M., Staikouras, C.: Fuzzy cognitive maps in banking business process performance measurement, pp. 161–200 (2010)

    Google Scholar 

  51. Motlagh, O., Tang, S.H., Ismail, N., Ramli, A.R.: An expert fuzzy cognitive map for reactive navigation of mobile robots. Fuzzy Sets Syst. 201, 105–121 (2012)

    MathSciNet  Google Scholar 

  52. Kardaras, D., Mentzas, G.: Using fuzzy cognitive maps to model and analyse business performance assessment. In: Chen, J., Mital, A. (eds) Advances in industrial engineering applications and practice II, pp. 63–68 (1997)

  53. Glykas, M.: Fuzzy cognitive strategic maps in business process performance measurement. Expert Syst. Appl. 40(1), 1–14 (2013)

    Google Scholar 

  54. Gerogiannis, V.C., Papadopoulou, S., Papageorgiou, E.I.: Identifying factors of customer satisfaction from smartphones: a fuzzy cognitive map approach. In: International Conference on Contemporary Marketing Issues, pp. 270–276 (2012)

  55. Papakostas, G.A., Boutalis, Y.S., Koulouriotis, D.E., Mertzios, B.G.: Fuzzy cognitive maps for pattern recognition applications. Int. J. Pattern Recognit Artif. Intell. 22(08), 1461–1486 (2008)

    Google Scholar 

  56. Rodriguez-Repiso, L., Setchi, R., Salmeron, J.L.: Modelling IT projects success with fuzzy cognitive maps. Expert Syst. Appl. 32(2), 543–559 (2007)

    Google Scholar 

  57. Kokkinos, K., Lakioti, E., Papageorgiou, E., Moustakas, K., Karayannis, V.: Fuzzy cognitive map-based modeling of social acceptance to overcome uncertainties in establishing waste biorefinery facilities. Front. Energy Res. 6, 1–17 (2018)

    Google Scholar 

  58. Olazabal, M., Neumann, M.B., Foudi, S., Chiabai, A.: Transparency and reproducibility in participatory systems modelling: the case of fuzzy cognitive mapping. Syst. Res. Behav. Sci. 35(6), 791–810 (2018)

    Google Scholar 

  59. Sona, P., Johnson, T., Vijayalakshmi, C.: Analyzing factors in production management using fuzzy cognitive mapping. Int. J. Pure Appl. Math. 118(23), 517–524 (2018)

    Google Scholar 

  60. Choi, Y., Lee, H., Irani, Z.: Big data-driven fuzzy cognitive map for prioritising IT service procurement in the public sector. Ann. Oper. Res. 270(1–2), 75–104 (2018)

    MathSciNet  Google Scholar 

  61. Khanzadi, M., Nasirzadeh, F., Dashti, M.S.: Fuzzy cognitive map approach to analyze causes of change orders in construction projects. J. Constr. Eng. Manag. 144(2), 04017111 (2018)

    Google Scholar 

  62. Pandey, P., Kumar, S., Shrivastav, S.: Forecasting using fuzzy time series for diffusion of innovation: case of Tata Nano car in India. Natl. Acad. Sci. Lett. 36(3), 299–309 (2013)

    Google Scholar 

  63. Pandey, P., Kumar, S., Shrivastava, S.: A unified strategy for forecasting of a new product. Decision 41(4), 411–424 (2014)

    Google Scholar 

  64. Pandey, P., Litoriya, R., Tiwari, A.: A framework for fuzzy modelling in agricultural diagnostics. J Européen des Systèmes Automatisés 51, 203–223 (2018)

    Google Scholar 

  65. Pandey, P., Litoriya, R.: A predictive fuzzy expert system for crop disease diagnostic and decision support. In: Fuzzy Expert Systems and Applications in Agricultural Diagnosis, IGI Global, pp. 175–194 (2019)

  66. Pandey, P., Litoriya, R.: An activity vigilance system for elderly based on fuzzy probability transformations. J. Intell. Fuzzy Syst. 36(3), 2481–2494 (2019)

    Google Scholar 

  67. Pandey, P., Litoriya, R.: Elderly care through unusual behavior detection: a disaster management approach using IoT and intelligence. IBM J. Res. Dev. 64(1), 1–11 (2019)

    Google Scholar 

  68. Pandey, P., Litoriya, R.: An IoT assisted system for generating emergency alerts using routine analysis. Wireless Pers. Commun. (2020). https://doi.org/10.1007/s11277-020-07064-0

    Article  Google Scholar 

  69. Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–59 (1994)

    Google Scholar 

  70. Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)

    Google Scholar 

  71. Kordylewski, H., Graupe, D., Liu, K.: A novel large-memory neural network as an aid in medical diagnosis applications. Trans. Inf. Technol. Biomed. 5(3), 202–209 (2001)

    Google Scholar 

  72. Ben-hur, A., Horn, D., Vapnik, V.: Support vector clustering. J. Mach. Learn. Res. 2, 125–137 (2001)

    MATH  Google Scholar 

  73. Rouaud, M.: Probability, statistics, and estimation: propagation of uncertainties in experimental measurement. In: Probability, statistics, and estimation, p. 24 (2017)

  74. Zhang, N., Luo, C.: Adaptive online time series prediction based on a novel dynamic fuzzy cognitive map. J. Intell. Fuzzy Syst. 36(6), 5291–5303 (2019)

    Google Scholar 

  75. Yin, W., Ping, C., Chiang, T., Kuokwee, W.: An evaluation of the role of fuzzy cognitive maps and Bayesian belief networks in the development of causal knowledge systems. J. Intell. Fuzzy Syst. pp. 1–16 (Pre-press)

  76. Reimann, S.: On the design of artificial auto-associative neuronal networks. Neural Netw. 11(4), 611–621 (1998)

    Google Scholar 

  77. Ozesmi, U.: Ecosystems in the mind: fuzzy cognitive maps of the Kizilirmak Delta Wetlands in Turkey. In: Proceedings of 1999 World Conference on Natural Resource Modelling (1999)

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, Jaypee University of Engineering & Technology, Raghogarh, Guna, India

    Prateek Pandey & Ratnesh Litoriya

Authors
  1. Prateek Pandey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ratnesh Litoriya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ratnesh Litoriya.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pandey, P., Litoriya, R. Fuzzy Cognitive Mapping Analysis to Recommend Machine Learning-Based Effort Estimation Technique for Web Applications. Int. J. Fuzzy Syst. 22, 1212–1223 (2020). https://doi.org/10.1007/s40815-020-00815-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-020-00815-y

Keywords

Search

Navigation