Optimizing Software Effort Estimation Models Using Firefly Algorithm

DOI: 10.4236/jsea.2015.83014   PDF   HTML   XML   3,157 Downloads   4,143 Views   Citations


Software development effort estimation is considered a fundamental task for software development life cycle as well as for managing project cost, time and quality. Therefore, accurate estimation is a substantial factor in projects success and reducing the risks. In recent years, software effort estimation has received a considerable amount of attention from researchers and became a challenge for software industry. In the last two decades, many researchers and practitioners proposed statistical and machine learning-based models for software effort estimation. In this work, Firefly Algorithm is proposed as a metaheuristic optimization method for optimizing the parameters of three COCOMO-based models. These models include the basic COCOMO model and other two models proposed in the literature as extensions of the basic COCOMO model. The developed estimation models are evaluated using different evaluation metrics. Experimental results show high accuracy and significant error minimization of Firefly Algorithm over other metaheuristic optimization algorithms including Genetic Algorithms and Particle Swarm Optimization.

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Ghatasheh, N. , Faris, H. , Aljarah, I. and Al-Sayyed, R. (2015) Optimizing Software Effort Estimation Models Using Firefly Algorithm. Journal of Software Engineering and Applications, 8, 133-142. doi: 10.4236/jsea.2015.83014.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Molokken, K. and Jorgensen, M. (2003) A Review of Software Surveys on Software Effort Estimation. 2003 International Symposium on Empirical Software Engineering, 30 September-1 October 2003, 223-230.
[2] Song, Q. and Shepperd, M. (2011) Predicting Software Project Effort: A Grey Relational Analysis Based Method. Expert Systems with Applications, 38, 7302-7316.
[3] Khatibi, V. and Jawawi, D.N. (2011) Software Cost Estimation Methods: A Review. Journal of Emerging Trends in Computing and Information Sciences, 2, 21-29.
[4] Afzal, W. and Torkar, R. (2011) On the Application of Genetic Programming for Software Engineering Predictive Modeling: A Systematic Review. Expert Systems with Applications, 38, 11984-11997.
[5] Albrecht, A. and Gaffney, J.E. (1983) Software Function, Source Lines of Code, and Development Effort Prediction: A Software Science Validation. IEEE Transactions on Software Engineering, SE-9, 639-648.http://dx.doi.org/10.1109/TSE.1983.235271
[6] Bailey, J.W. and Basili, V.R. (1981) A Meta-Model for Software Development Resource Expenditures. Proceedings of the 5th International Conference on Software Engineering, Piscataway, 107-116.
[7] Ruchika Malhotra, A.J. (2011) Software Effort Prediction Using Statistical and Machine Learning Methods. International Journal of Advanced Computer Science and Applications (IJACSA), 2.
[8] Yadav, C.S. and Singh, R. (2014) Tuning of Cocomo ii Model Parameters for Estimating Software Development Effort Using GA for Promise Project Data Set. International Journal of Computer Applications, 90, 37-43. http://dx.doi.org/10.5120/15542-4367
[9] Wang, F.-S. and Chen, L.-H. (2013) Heuristic Optimization. In: Dubitzky, W., Wolkenhauer, O., Cho, K.-H. and Yokota, H., Eds., Encyclopedia of Systems Biology, Springer, New York, 885-885.
[10] Uysal, M. (2010) Estimation of the Effort Component of the Software Projects Using Heuristic Algorithms. INTECH Open Access Publisher, Croatia.
[11] Alaa, F. and Al-Afeef, A. (2010) A GP Effort Estimation Model Utilizing Line of Code and Methodology for NASA Software Projects. IEEE 10th International Conference on Intelligent Systems Design and Applications (ISDA), Cairo, 29 November-1 December 2010, 290-295.
[12] Bhattacharya, P., Srivastava, P. and Prasad, B. (2012) Software Test Effort Estimation Using Particle Swarm Optimization. In: Satapathy, S., Avadhani, P. and Abraham, A., Eds., Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012 (INDIA 2012), Visakhapatnam, January 2012, Vol. 132 of Advances in Intelligent and Soft Computing, 827-835. Springer, Berlin and Heidelberg.
[13] Maleki, I., Ghaffari, A. and Masdari, M. (2014) A New Approach for Software Cost Estimation with Hybrid Genetic Algorithm and Ant Colony Optimization. International Journal of Innovation and Applied Studies, 5, 72-81.
[14] Yang, X.-S. and Deb, S. (2009) Cuckoo Search via Levy Flights. World Congress on Nature Biologically Inspired Computing, NaBIC 2009, Coimbatore, 9-11 December 2009, 210-214.
[15] Kennedy, J. and Eberhart, R. (1995) Particle Swarm Optimization. IEEE International Conference on Neural Networks, 4, 1942-1948.
[16] Yang, X.-S. (2010) A New Metaheuristic Bat-Inspired Algorithm. In: Gonzlez, J., Pelta, D., Cruz, C., Terrazas, G. and Krasnogor, N., Eds., Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Vol. 284 of Studies in Computational Intelligence, 65-74. Springer, Berlin and Heidelberg.
[17] Yang, X.-S. (2009) Firefly Algorithms for Multimodal Optimization. In: Watanabe, O. and Zeugmann, T., Eds., Stochastic Algorithms: Foundations and Applications, Vol. 5792 of Lecture Notes in Computer Science, 169-178. Springer, Berlin and Heidelberg.
[18] Sheta, A., Rine, D. and Ayesh, A. (2008) Development of Software Effort and Schedule Estimation Models Using Soft Computing Techniques. IEEE Congress on Evolutionary Computation, CEC 2008 (IEEE World Congress on Computational Intelligence), Hong Kong, 16 June 2008, 1283-1289.
[19] Oliveira, A.L.I., Braga, P.L., Lima, R.M.F. and Cornélio, M.L. (2010) GA-Based Method for Feature Selection and Parameters Optimization for Machine Learning Regression Applied to Software Effort Estimation. Information and Software Technology, 52, 1155-1166.
[20] Huang, X., Ho, D., Ren, J. and Capretz, L. (2006) A Soft Computing Framework for Software Effort Estimation. Soft Computing, 10, 170-177. http://dx.doi.org/10.1007/s00500-004-0442-z
[21] Dave, V. and Dutta, K. (2014) Neural Network Based Models for Software Effort Estimation: A Review. Artificial Intelligence Review, 42, 295-307. http://dx.doi.org/10.1007/s10462-012-9339-x
[22] Keung, J., Kocaguneli, E. and Menzies, T. (2013) Finding Conclusion Stability for Selecting the Best Effort Predictor in Software Effort Estimation. Automated Software Engineering, 20, 543-567.
[23] Boehm, B., Abts, C., Clark, B. and Devnani-Chulani, S. (1997) COCOMO II Model Definition Manual. University of Southern California, Los Angeles.
[24] Sheta, A.F. (2006) Estimation of the COCOMO Model Parameters Using Genetic Algorithms for NASA Software Projects. Journal of Computer Science, 2, 118-123.

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