Application of Ant Colony Optimization Techniques to Predict Software Cost Estimation

  • V. Venkataiah
  • Ramakanta Mohanty
  • J. S. Pahariya
  • M Nagaratna
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 5)


In modern society, machine learning techniques employed to predict Software Cost Estimation viz. Decision Tree, K-Nearest Neighbor, Support Vector Machine, Neural Networks, and Fuzzy Logic and so on. Every technique has contributed good work in the significant field of software cost estimation. The Computational Intelligence techniques also contributed a great extent in standard-alone. Still there is an immense scope to apply optimization techniques. In this paper, we propose Ant colony optimization techniques to predict software cost estimation based on three datasets collected from literature. For each datasets, we performed tenfold cross validation on International Software Benchmarking Standards Group (ISBSG) dataset and threefold cross validation performed on IBM Data Processing Service (IBMDPS) and COCOMO 81 datasets. The method is validated with real datasets using Root Mean Square Error (RMSE).


Software Cost Estimation (SEC) Ant Colony Optimization Technique (ACOT) Travelling Sales Person (TSO) Root Mean Square Error (RMSE) 


  1. 1.
    Ali, I., Azeddine, Z.: Software Cost Estimation by Classical and Fuzzy Analogy for Web Hypermedia Applications: A replicated study. IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 117–121, (2013)Google Scholar
  2. 2.
    Attarzadeh, I., Merhanzadeh, A., Ali, B.: Proposing an Enhanced Artificial Neural Network Prediction Model Improve the Accuracy in Software Effort Estimation. IEEE Fourth International Conference on Computational Intelligence, Communication Systems and Networks, pp. 167–172, (2012)Google Scholar
  3. 3.
    Dejaeger, K., Verbeke, W., David, M., Bart B.: Data Mining Techniques for Software Effort Estimation: A Comparative Study. IEEE Transactions on Software Engineering, Vol. 38, No. 2, March/April (2012)Google Scholar
  4. 4.
    Attarzadeh, I., Hock, O. S.: Proposing a New Software Cost Estimation Model Based for Software Cost Estimation. IEEE 2nd International Conference on Computer and Electrical Engineering, pp. 112–116, (2009)Google Scholar
  5. 5.
    Mohanty, R. K., Ravi, V., Patra, M. R.: The Application of Intelligent and Soft-computing Technique to Software Engineering Problems: A state of the art Report. International Journal of Information and Decision Sciences, Vol. 2, Number 3, pp. 232–272 (2009)Google Scholar
  6. 6.
    Attarzadeh, I., Merhanzadeh, A., Ali, B.: Proposing an Enhanced Artificial Neural Network Prediction Model to Improve the Accuracy in Software Effort Estimation. IEEE Fourth International Conference on Computational Intelligence, Communication Systems and Networks, pp. 167–172, (2012)Google Scholar
  7. 7.
    Hari, CH. V. M. K., Tegjyot S. S., Kaushal B.S. S., Abhishek S.: CPN-a hybrid model for software cost estimation. IEEE International Conference on Recent Advances in Intelligent Computational Systems (RAICS), pp. 902–906, Sep 22, (2011)Google Scholar
  8. 8.
    Jiang, G. Wang,Y., Haitao.: Research on Software Evolution Model on Case Based Reasoning. IEEE 2nd International Conference on WRI World Congress on Software Engineering, pp. 338–341, (2010)Google Scholar
  9. 9.
    Venkataiah, V., Mohanty, R.K., Nagaratna, M.: Application of Practical Swarm Optimization to predict Software Cost Estimation. 6th IEEE International Conference on Communication Systems and Network Technologies, 05–07, March (2016)Google Scholar
  10. 10.
    Lalit Patil, V., Nitin Shivale, M., JoshiJ, D., Khanna, V.: Improving the Accuracy of CBSD Effort Estimation using Fuzzy Logic. IEEE International Advance Computing Conference, pp. 1395–1391, (2014)Google Scholar
  11. 11.
    Manikavelan, D., Ponnusamy, R.: To Find the Accuracy Software Cost Estimation Using Differential Evaluation Algorithm. IEEE International Conference on Computational Intelligence and Computing Research, (2013)Google Scholar
  12. 12.
    Azzeh, Mod.: Software Cost Estimation Based on Use Case Points for Global Software Development. IEEE 5th International Conference on Computer Science and Information Technology (CSIT), pp. 214–218, ISBN: 978-1-4673-5825-5, (2013)Google Scholar
  13. 13.
    Khatib Bardsiri, V., Jawawi, D.N.A., Hashim, S.Z.M., Khatibi, E.: Increasing the accuracy of software development effort estimation using project clustering. The Institution of Engineering and Technology Journal, Vol.6, Iss.6, pp. 461–473, (2012)Google Scholar
  14. 14.
    Attarzadeh, I., Hock, O. S.: Proposing a New Software Cost Estimation Model Based on Artificial Neural Networks. IEEE 2nd International Conference on Computer Engineering and Technology, Vol. 3, pp. 287–291, (2010)Google Scholar
  15. 15.
    Hari, CH. V. M. K., Prasad Reddy, P. V. G. D., Jagadeesh, M., SriRam Ganesh, G.: IntervalType-2 Fuzzy Logic for Software Cost Estimation Using TSFC with Mean and Standard Deviation. IEEE International Conference on Advances in Recent Technologies in Communication and computing, pp. 40–44, (2010)Google Scholar
  16. 16.
    Zhang, B., Zhang, R.: Evolution Model of Software cost estimation methods based on Fuzzy-Grey Theory. IEEE Fourth International Conference on Internet Computing for Science and Engineering, pp. 52–55, (2009)Google Scholar
  17. 17.
    Pahariya, J.S., Ravi, V., Carr, M.: Software Cost Estimation using Computational Intelligence Techniques. IEEE Conference on World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), pp. 849–854, (2009)Google Scholar
  18. 18.
    Malathi, S., Lijin, B.S.: An Efficient Method for the Estimation of Effort in Software Cost. International Journal of Advance Research in Computer Science and Management Studies Volume 2, pp. 330–335, February (2014)Google Scholar
  19. 19.
    Nikolaos, M., Mamalikidis, I., Angelis, L.: A framework for comparing multiple cost estimation methods using an automated visualization toolkit. Information and Software Technology Vol. 57, pp. 310–328, (2015)Google Scholar
  20. 20.
    Zhang, W., Yang, Y., Wang, Q.: Using Bayesian Regression and EM algorithm with missing handling for software effort prediction. Information and Software Technology, pp. 58–70, February (2015)Google Scholar
  21. 21.
    Miandoab, E., Gharehchopogh, F. G.: A Novel Hybrid Algorithm for Software Cost Estimation Based on Cuckoo Optimization and K- Nearest Neighbors Algorithms. International Journal of Engineering, Technology & applied Science Research. Vol. 2, No. 3, pp. 1018–1022, (2016)Google Scholar
  22. 22.
    Boehm, B.: Software Engineering Economics. Prentice Hall, (1981)Google Scholar
  23. 23.
    Coloni, A., Dorigo, M., Maniezzo, V.: Ant system: Optimization by a colony of cooperating agent. IEEE Trans. Systems Man and Cybemetics-Part B: Cybemetics, vol. 26, No. 1, pp. 29–41, (1996)Google Scholar
  24. 24.
    Dorigo, M., Dicaro, G.: The Ant Colony Optimization Meta-Heuristic. In Corne, D., Dorigo, M., Glover, F. editors, New Ideas in Optimization, McGraw-Hill, pp. 11–32, (1999)Google Scholar
  25. 25.
    Dorigo, M., Gambardella, L. M.: Ant Colony System: A cooperative learning approach to the Traveling Salesman problem. IEEE Transactions on Evolutionary Computation, vol. 1, No.1, pp. 53–66, (1997)Google Scholar
  26. 26.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann Series in Data Management Systems, (2006)Google Scholar
  27. 27.
    Bhardwaj, M., Ajay, R.: Estimation of Testing and Rework Efforts for Software Development Projects. Asian Journal of Computer Science and Information Technology, ISSN.2249–5126, pp. 33–37 (2015)Google Scholar
  28. 28.
    Pressman, R. S.: Software Engineering: A Practitioner’s Approach. McGraw-Hill series in Computer Science, New York, (2001)Google Scholar
  29. 29.
    Sheta, A.F., David, R., Ayesh, A.: Development of software Effort and Schedule Estimation models using Soft Computing Techniques. IEEE Conference on Evolutionary Computation, pp. 1283–1288, (2008)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • V. Venkataiah
    • 1
  • Ramakanta Mohanty
    • 2
  • J. S. Pahariya
    • 3
  • M Nagaratna
    • 4
  1. 1.Computer Science and EngineeringCMR College of Engineering and TechnologyMedchalIndia
  2. 2.Computer Science and EngineeringKeshav Memorial Institute of TechnologyNarayanagudaIndia
  3. 3.Computer Science and EngineeringRustamji Institute of TechnologyTekanpurIndia
  4. 4.Computer Science and EngineeringJNTUH College of EngineeringKukatpallyIndia

Personalised recommendations