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Evolutionary Computing Techniques for Software Effort Estimation


Affiliations
1 I.K.G. Punjab Technical University, Jalandhar, Punjab, India
2 Sri Guru Granth Sahib World University, Fatehgarh Sahib, Punjab, India
 

Reliable and accurate estimation of software has always been a matter of concern for industry and academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all types of datasets and environments. Since the motive of estimation model is to minimize the gap between actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization, Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of COCOMO Model. The performance of these techniques has been analysed by established performance measure. The results obtained have been validated by using data of Interactive voice response (IVR) projects. Evolutionary techniques have been found to be more accurate than existing estimation models.

Keywords

Machine Learning, COCOMO, MMRE, Evolutionary Computing.
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  • Aljahdali, S., Sheta, A.F.: Software effort estimation by tuning COOCMO model parameters using differential evolution. In: ACS/IEEE International Conference on Computer Systems and Applications - AICCSA 2010, pp. 1–6 (2010).

  • Azzeh, M.: A replicated assessment and comparison of adaptation techniques for analogy-based effort estimation. Empirical Software Engineering 17(1-2), 90–127 (2012).

  • Bhatnagar, R., Bhattacharjee, V., Ghose, M.K.: Software development effort estimation neural network vs. regression modeling approach. International Journal of Engineering Science and Technology 2(7), 2950–2956 (2010).

  • Bhattacharya, P., Srivastava, P.R., Prasad, B.: Software Test Effort Estimation Using Particle Swarm Optimization. In: Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012 (INDIA 2012) held in Visakhapatnam, India, January 2012, pp. 827–835. Springer Berlin Heidelberg, Berlin, Heidelberg (2012).

  • Braga, P.L., Oliveira, A.L., Meira, S.R.: A GA-based feature selection and parameters optimization for support vector regression applied to software effort estimation. In: Proceedings of the 2008 ACM symposium on Applied computing, pp. 1788–1792 (2008).

  • Braga, P.L., Oliveira, A.L.I., Ribeiro, G.H.T., Meira, S.R.L.: Bagging predictors for estimation of software project effort. In: International Joint Conference on Neural Networks, pp. 1595–1600. Florida,USA (2007).

  • Burgess, C.J., Lefley, M.: Can genetic programming improve software effort estimation? A comparative evaluation. Information and Software Technology 43(14), 863 – 873 (2001).

  • Chalotra, S., Sehra, S., Brar, Y., Kaur, N.: Tuning of COCOMO Model Parameters by using Bee Colony Optimization. Indian Journal of Science & Technology 8(14), 1–5 (2015).

  • Chen, W.N., Zhang, J.: Ant Colony Optimization for Software Project Scheduling and Staffing with an Event-Based Scheduler. IEEE Transactions on Software Engineering 39(1), 1–17 (2013).

  • Corazza, A., Martino, S.D., Ferrucci, F., Gravino, C., Sarro, F., Mendes, E.: Using tabu search to configure support vector regression for effort estimation. Empirical Software Engineering 18(3), 506–546 (2013).

  • Dewan, N., Sehra, S.K.: Ant Colony Optimization Based Software Effort Estimation. International Journal of Computer Science And Technology 5(3), 53–56 (2014).

  • Dizaji, Z.A., Gharehchopogh, F.S.: A hybrid of ant colony optimization and chaos optimization algorithms approach for software cost estimation. Indian Journal of Science & Technology 8(2), 128–133 (2015).

  • Dorigo, M., Blum, C.: Ant colony optimization theory: A survey. Theoretical Computer Science 344(2), 243 – 278 (2005).

  • E. Mendes I. Watson, C.T.N.M.S.C.: A comparative study of cost estimation models for web hypermedia applications. Empirical Software Engineering 8(2), 163–196 (2003).

  • Elish, M.: Improved estimation of software project effort using multiple additive regression trees. Expert Systems with Applications 36(7), 10,774–10,778 (2009).

  • Ferrucci, F., Gravino, C., Sarro, F.: How multi-objective genetic programming is effective for software development effort estimation? In: Search Based Software Engineering, pp. 274–275. Springer (2011).

  • Gupta, R., Chaudhary, N., Pal, S.K.: Hybrid model to improve bat algorithm performance. In: International Conference on Advances in Computing, Communications and Informatics, pp. 1967–1970. New Delhi, India (2014).

  • Huang, S.J., Chiu, N.H., Chen, L.W.: Integration of the grey relational analysis with genetic algorithm for software effort estimation. European Journal of Operational Research 188(3), 898–909 (2008).

  • Jørgensen, M.: Contrasting ideal and realistic conditions as a means to improve judgment-based software development effort estimation. Information and Software Technology 53(12), 1382–1390 (2011).

  • Jorgensen, M.: A review of studies on expert estimation of software development effort. Journal of Systems and Software 70(1), 37–60 (2004).

  • Jorgensen, M., Moløkken-Østvold, K.: How large are software cost overruns? A review of the 1994 {CHAOS} report. Information and Software Technology 48(4), 297 – 301 (2006).

  • Kaur, M., Sehra, S.K.: Particle swarm optimization based effort estimation using Function Point analysis. In: Issues and Challenges in Intelligent Computing Techniques (ICICT), 2014 International Conference on, pp. 140–145 (2014).

  • Kennedy, J., Eberhat, R.C.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, pp. 1942–1948 (1995).

  • Menzies, T., Chen, Z., Hihn, J., Lum, K.: Selecting best practices for effort estimation. Software Engineering, IEEE Transactions on 32(11), 883–895 (2006).

  • Oliveira, A.L.I., Braga, P.L., Lima, R.M.F., Corn´elio, M.L.: GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation. Information and Software Technology 52(11), 1155 – 1166 (2010).

  • Park, H., Baek, S.: An empirical validation of a neural network model for software effort estimation. Expert Syst. Appl. 35(3), 929–937 (2008).

  • Pertiwi, A.P., Suyanto: Globally Evolved Dynamic Bee Colony Optimization, pp.52–61. Springer Berlin Heidelberg, Berlin, Heidelberg (2011).

  • Prasad Reddy P.V.G.D, Hari CH.V.M.K., Srinivasa Rao T.: Multi Objective Particle Swarm Optimization for Software Cost Estimation. International Journal of Computer Applications (0975 – 8887) 32(3), 13–17 (2011).

  • Sarro, F.: Search-based Approaches for Software Development Effort Estimation. In: Proceedings of the 12th International Conference on Product Focused Software Development and Process Improvement, Profes ’11, pp. 38–43. ACM, New York, NY, USA (2011).

  • Sheta, A., Rine, D., Ayesh, A.: Development of software effort and schedule estimation models using Soft Computing Techniques. In: Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on, pp. 1283–1289 (2008).

  • Sheta, A.F.: Estimation of the COCOMO model parameters using genetic algorithms for NASA software projects. Journal of Computer Science 2(2), 118–123 (2006).

  • Srivastava, D.K., Chauhan, D.S., Singh, R.: VRS Model: A Model for Estimation of Efforts and Time Duration in Development of IVR Software System. Int. J. of Software Engineering, IJSE 5(1) (2012).


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  • Evolutionary Computing Techniques for Software Effort Estimation

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Authors

Sumeet Kaur Sehra
I.K.G. Punjab Technical University, Jalandhar, Punjab, India
Yadwinder Singh Brar
I.K.G. Punjab Technical University, Jalandhar, Punjab, India
Navdeep Kaur
Sri Guru Granth Sahib World University, Fatehgarh Sahib, Punjab, India

Abstract


Reliable and accurate estimation of software has always been a matter of concern for industry and academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all types of datasets and environments. Since the motive of estimation model is to minimize the gap between actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization, Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of COCOMO Model. The performance of these techniques has been analysed by established performance measure. The results obtained have been validated by using data of Interactive voice response (IVR) projects. Evolutionary techniques have been found to be more accurate than existing estimation models.

Keywords


Machine Learning, COCOMO, MMRE, Evolutionary Computing.

References