Chaos-Based Modified Morphological Genetic Algorithm for Effort Estimation in Agile Software Development

  • Saurabh BilgaiyanEmail author
  • Prabin Kumar Panigrahi
  • Samaresh Mishra
Part of the Intelligent Systems Reference Library book series (ISRL, volume 185)


One of the most critical and important aspects of any software development project is the estimation of cost and effort, as the success or failure of the entire project is largely dependent on the accuracy of these estimations. For any software development project, several methods such as waterfall, prototyping etc. exist, but the agile methods have prevailed in terms of its efficiency and implementation in solving the problems related to the projects thus substituting the traditional methodologies. Agile methods have become much popular recently because of its ability to adopt to the changing dynamics (requirements) of software projects. This dynamic nature makes the task of estimation even more challenging than the traditional methodologies present. Thus, it becomes convenient to accurately estimate the effort and cost while adopting the agile methods, for which various techniques have already been proposed such as analogy, dis-aggregation, expert opinion etc, but none among the same have a proper mathematical model. This work has presented a novice method from the domain of evolutionary algorithms. The work is based on mathematical morphology (MM) consisting of a hybrid-artificial neuron (Dilation-Erosion perceptron (DEP)) extended from the concept of complete lattice theory (CLT). Authors have presented a chaotically modified genetic algorithm (CMGA) to build the DEP-CMGA model for solving the software development effort estimation (SDEE) problem. Calibration of the proposed model was done using data collected from 21 software projects based on agile software development (ASD). Four different statistics were used for determining the precision of the model and the results were compared with the one’s obtained using the best available model in literature.


Evolutionary algorithms SDEE Complete lattice theory Mathematical morphology Agile software development 


  1. 1.
    Dragicevic, S., Turic, S.C.M.: Bayesian network model for task effort estimation in agile software development. J. Syst. Softw. (Elsevier) 127(1), 109–119 (2017)CrossRefGoogle Scholar
  2. 2.
    Bilgaiyan, S., Mishra, S. and Das, M.: A review of software cost estimation in agile software development using soft computing techniques. In: 2nd International Conference on Computational Intelligence and Networks (CINE), pp. 112–117. IEEE (2016)Google Scholar
  3. 3.
    Strode, D.E.: A dependency taxonomy for agile software development projects. Inf. Syst. Front. (Springer) 18(1), 23–46 (2016)CrossRefGoogle Scholar
  4. 4.
    Dingsoyr, T., Moe, N.B., Fagri, T.E., et al.: Exploring software development at the very large-scale: a revelatory case study and research agenda for agile method adaptation. Empir. Softw. Eng. (Springer) 23(1), 490–520 (2018)CrossRefGoogle Scholar
  5. 5.
    Alahyari, H., Svensson, R.B., Gorschek, T.: A study of value in agile software development organizations. J. Syst. Softw. (Elsevier) 125(1), 271–288 (2017)CrossRefGoogle Scholar
  6. 6.
    Hoda, R., Salleh, N., Grundy, J., et al.: Systematic literature reviews in agile software development: a tertiary study. Inf. Softw. Technol. (Elsevier) 85(1), 60–70 (2017)CrossRefGoogle Scholar
  7. 7.
    Dominguez, J.: The Curious case of the chaos report (2009).
  8. 8.
    Araujo, R.A., Oliveira, A.L.I., Soares, S. et. al.: An evolutionary morphological approach for software development cost estimation. Neural Netw. (Elsevier) 32(1), 285–291 (2012)Google Scholar
  9. 9.
    Araujo, R.A.: A class of hybrid morphological perceptrons with application in time series forecasting. Knowl.-Based Syst. (Elsevier) 24(4), 513–529 (2011)CrossRefGoogle Scholar
  10. 10.
    Banon, G.J.F., Barrera, J.: Decomposition of mappings between complete lattices by mathematical morphology part I. General lattices. Signal Process. (Elsevier) 30(3), 299–327 (1993)Google Scholar
  11. 11.
    Zia, Z.K., Tipu, S.K., Zia, S.K.: An effort estimation model for agile software development. Adv. Comput. Sci. Its Appl. (World Sciences) 2(1), 1–6 (2012)Google Scholar
  12. 12.
    Panda, A., Satapathy, S.M., Rath, S.K.: Empirical validation of neural network models for agile software effort estimation based on story points. In: 3rd International Conference on Recent Trends in Computing, pp. 772–781. Elsevier (2015)Google Scholar
  13. 13.
    Reeves, C.: Genetic algorithms. Handbook of Metaheuristics (Springer) 57(1), 55–82 (2011)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Sharma, A., Bawa, R.K.: A roadmap for agility estimation and method selection for secure agile development using AHP and ANN. Data Eng. Intell. Comput. (Springer) 542, 237–245 (2017)Google Scholar
  15. 15.
    Araujo, R.A., Soares, S., Oliveira, A.L.I.: Hybrid morphological methodology for software development cost estimation. Expert. Syst. Appl. (Elsevier) 39(1), 6129–6139 (2012)CrossRefGoogle Scholar
  16. 16.
    Braga, P.L., Oliveira, A.L.I., Meira, S.R.L.: Software effort estimation using machine learning techniques with robust confidence intervals. In: International Conference on Tools with Artificial Intelligence (ICTAI), vol. 8, pp. 1595–1600. IEEE (2007)Google Scholar
  17. 17.
    Oliveira, A.L.I., Braga, P.L. et. al.: GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation. Inf. Softw. Technol. (Elsevier) 52(1), 6129–6139 (2010)Google Scholar
  18. 18.
    Araujo, R.A, Oliveira, A.L.I., Soares S.C.B.: A morphological-rank-linear approach for software development cost estimation. In: 21st IEEE International Conference on Tools with Artificial Intelligence, pp. 630–636 (2009)Google Scholar
  19. 19.
    Araujo, R.A., Oliveira, A.L.I., Soares, S.: Gradient based morphological approach for software development cost estimation. In: Proceedings of the Symposium on Applied Computing, pp. 588–594. IEEE (2011)Google Scholar
  20. 20.
    Araujo, R.A., Oliveira, A.L.I., Soares, S.: Hybrid intelligent design of morphological-rank-linear perceptrons for software development cost estimation. In: 22nd International Conference on Tools with Artificial Intelligence, pp. 160–167. IEEE (2010)Google Scholar
  21. 21.
    Araujo, R.A., Oliveira, L.I., Soares, S.: A shift-invariant morphological system for software development cost estimation. Expert. Syst. Appl. (Elsevier) 38(4), 4162–4168 (2011)CrossRefGoogle Scholar
  22. 22.
    Choudharia, J., Suman, U.: Story points based effort estimation model for software maintenance. Procedia Technol. (Elsevier) 4, 761–765 (2012)CrossRefGoogle Scholar
  23. 23.
    Estevao, P.S., Esmi, L.: Morphological perceptrons with competitive learning: lattice-theoretical framework and constructive learning algorithm. Inf. Sci. (Elsevier) 181(10), 1929–1950 (2011)MathSciNetzbMATHGoogle Scholar
  24. 24.
    Leung, F.H.F., Lam, H.K., Ling, S.H.: Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Trans. Neural Netw. 14(1), 79–88 (2003)CrossRefGoogle Scholar
  25. 25.
    Gao, W., Liu, S., Huang, L.: Particle swarm optimization with chaotic opposition-based population initialization and stochastic search technique. Commun. Nonlinear Sci. Numer. Simul. 17(11), 4316–4327 (2012)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Saurabh Bilgaiyan
    • 1
    Email author
  • Prabin Kumar Panigrahi
    • 2
  • Samaresh Mishra
    • 1
  1. 1.School of Computer EngineeringKalinga Institute of Industrial Technology, Deemed to be UniversityBhubaneswarIndia
  2. 2.C. V. Raman College of EngineeringBhubaneswarIndia

Personalised recommendations