A Dataset-Independent Model for Estimating Software Development Effort Using Soft Computing Techniques


During the recent years, numerous endeavours have been made in the area of software development effort estimation for calculating the software costs in the preliminary development stages. These studies have resulted in the offering of a great many of the models. Despite the large deal of efforts, the substantial problems of the offered methods are their dependency on the used data collection and, sometimes, their lack of appropriate efficiency. The current article attempts to present a model for software development effort estimation through making use of evolutionary algorithms and neural networks. The distinctive characteristic of this model is its lack of dependency on the collection of data used as well as its high efficiency. To evaluate the proposed model, six different data collections have been used in the area of software effort estimation. The reason for the application of several data collections is related to the investigation of the model performance independence of the data collection used. The evaluation scales have been MMRE, MdMRE and PRED (0.25). The results have indicated that the proposed model, besides delivering high efficiency in contrast to its counterparts, produces the best responses for all of the used data collections.

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  • [1] X.-Y. Jing, F. Qi, F. Wu, and B. Xu, “Missing Data Imputation Based on Low-Rank Recovery and Semi-Supervised Regression for Software Effort Estimation” in Proceedings of the 38th International Conference on Software Engineering (ICSE 2016), 2016, pp. 607–618. https://doi.org/10.1145/2884781.2884827

  • [2] F. Qi, X.-Y. Jing, X. Zhu, X. Xie, B. Xu, and S. Ying, “Software Effort Estimation Based on Open Source Projects: Case Study of Github,” Information and Software Technology, vol. 92, pp. 145–157, Dec. 2017. https://doi.org/10.1016/j.infsof.2017.07.015

  • [3] F. Zare, H. K. Zare, and M. S. Fallahnezhad, “Software Effort Estimation Based on the Optimal Bayesian Belief Network,” Applied Soft Computing, vol. 49, pp. 968–980, Dec. 2016. https://doi.org/10.1016/j.asoc.2016.08.004

  • [4] M. Jørgensen, “The Influence of Selection Bias on Effort Overruns in Software Development Projects,” Information and Software Technology, vol. 55, no. 9, pp. 1640–1650, Sep. 2013. https://doi.org/10.1016/j.infsof.2013.03.001

  • [5] S. Grimstad, M. Jørgensen, and K. Moløkken-Østvold, “Software Effort Estimation Terminology: The Tower of Babel,” Information and Software Technology, vol. 48, no. 4, pp. 302–310, Apr. 2006. https://doi.org/10.1016/j.infsof.2005.04.004

  • [6] B. Kitchenham, S. Lawrence Pfleeger, B. McColl, and S. Eagan, “An Empirical Study of Maintenance and Development Estimation Accuracy,” Journal of Systems and Software, vol. 64, no. 1, pp. 57–77, Oct. 2002. https://doi.org/10.1016/S0164-1212(02)00021-3

  • [7] M. Jorgensen and M. Shepperd, “A Systematic Review of Software Development Cost Estimation Studies,” IEEE Transactions on Software Engineering, vol. 33, no. 1, pp. 33–53, Jan. 2007. https://doi.org/10.1109/TSE.2007.256943

  • [8] A. B. Nassif, M. Azzeh, L. F. Capretz, and D. Ho, “Neural Network Models for Software Development Effort Estimation: A Comparative Study,” Neural Computing and Applications, vol. 27, no. 8, pp. 2369–2381, Nov. 2015. https://doi.org/10.1007/s00521-015-2127-1

  • [9] M. Jørgensen and D. I. Sjøberg, “Impact of Effort Estimates on Software Project Work,” Information and Software Technology, vol. 43, no. 15, pp. 939–948, Dec. 2001. https://doi.org/10.1016/S0950-5849(01)00203-8

  • [10] J. Khan, Z. A. Shaikh, and A. B. Nauman, “Development of Intelligent Effort Estimation Model Based on Fuzzy Logic Using Bayesian Networks” in International Conference on Advanced Software Engineering and Its Applications, Springer, 2011, pp. 74–84. https://doi.org/10.1007/978-3-642-27207-3_9

  • [11] R. Fuentetaja, D. Borrajo, C. L. López, and J. Ocón, “Multi-Step Generation of Bayesian Networks Models for Software Projects Estimations,” International Journal of Computational Intelligence Systems, vol. 6, no. 5, pp. 796–821, 2013. https://doi.org/10.1080/18756891.2013.805583

  • [12] D. Eck, et al., Parametric Estimating Handbook, The International Society of Parametric Analysts, 2009.

  • [13] J. Lynch, “Chaos Manifesto,” The Standish Group, 2009.

  • [14] J. Moeyersoms, E. Junqué de Fortuny, K. Dejaeger, B. Baesens, and D. Martens, “Comprehensible Software Fault and Effort Prediction: A Data Mining Approach,” Journal of Systems and Software, vol. 100, pp. 80–90, Feb. 2015. https://doi.org/10.1016/j.jss.2014.10.032

  • [15] S. R. Chidamber and C. F. Kemerer, “A Metrics Suite for Object Oriented Design,” IEEE Transactions on Software Engineering, vol. 20, no. 6, pp. 476–493, Jun. 1994. https://doi.org/10.1109/32.295895

  • [16] T. Menzies, Z. Chen, J. Hihn, and K. Lum, “Selecting Best Practices for Effort Estimation,” IEEE Transactions on Software Engineering, vol. 32, no. 11, pp. 883–895, Nov. 2006. https://doi.org/10.1109/TSE.2006.114

  • [17] C. Lopez-Martin, C. Isaza, and A. Chavoya, “Software Development Effort Prediction of Industrial Projects Applying a General Regression Neural Network,” Empirical Software Engineering, vol. 17, no. 6, pp. 738–756, Dec. 2012. https://doi.org/10.1007/s10664-011-9192-6

  • [18] A. Idri, F. azzahra Amazal, and A. Abran, “Analogy-Based Software Development Effort Estimation: A Systematic Mapping and Review,” Information and Software Technology, vol. 58, pp. 206–230, Feb. 2015. https://doi.org/10.1016/j.infsof.2014.07.013

  • [19] A. Khatibi Bardsiri, S. M. Hashemi, and M. Razzazi, “GVSEE: A New Global Model to Estimate Software Services Development Effort,” Journal of the Chinese Institute of Engineers, vol. 39, no. 6, pp. 765–776, 2016. https://doi.org/10.1080/02533839.2016.1176873

  • [20] J. Keung, E. Kocaguneli, and T. Menzies, “Finding Conclusion Stability for Selecting the Best Effort Predictor in Software Effort Estimation,” Automated Software Engineering, vol. 20, no. 4, pp. 543–567, May 2012. https://doi.org/10.1007/s10515-012-0108-5

  • [21] D. Wu, J. Li, and Y. Liang, “Linear Combination of Multiple Case-Based Reasoning With Optimized Weight for Software Effort Estimation,” The Journal of Supercomputing, vol. 64, no. 3, pp. 898–918, Dec. 2010. https://doi.org/10.1007/s11227-010-0525-9

  • [22] L. A. Zadeh, “Soft Computing and Fuzzy Logic,” in Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi A. Zadeh, World Scientific, 1996, pp. 796–804. https://doi.org/10.1142/9789814261302_0042

  • [23] A. F. Sheta, “Estimation of the COCOMO Model Parameters Using Genetic Algorithms for NASA Software Projects,” Journal of Computer Science, vol. 2, no. 2, pp. 118–123, Feb. 2006. https://doi.org/10.3844/jcssp.2006.118.123

  • [24] J. J. Dolado and L. Fernandez, “Genetic Programming, Neural Networks and Linear Regression in Software Project Estimation” in Proceedings of International Conference on Software Process Improvement, Research, Education and Training, 1998.

  • [25] A. Sheta, D. Rine, and A. Ayesh, “Development of Software Effort and Schedule Estimation Models Using Soft Computing Techniques” in 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 1283–1289, Jun. 2008.https://doi.org/10.1109/CEC.2008.4630961

  • [26] N.-H. Chiu and S.-J. Huang, “The Adjusted Analogy-Based Software Effort Estimation Based on Similarity Distances,” Journal of Systems and Software, vol. 80, no. 4, pp. 628–640, Apr. 2007. https://doi.org/10.1016/j.jss.2006.06.006

  • [27] S.-J. Huang and N.-H. Chiu, “Optimization of Analogy Weights by Genetic Algorithm for Software Effort Estimation,” Information and Software Technology, vol. 48, no. 11, pp. 1034–1045, Nov. 2006. https://doi.org/10.1016/j.infsof.2005.12.020

  • [28] Q. Song and M. Shepperd, “Predicting Software Project Effort: A Grey Relational Analysis Based Method,” Expert Systems with Applications, vol. 38, no. 6, pp. 7302–7316, Jun. 2011. https://doi.org/10.1016/j.eswa.2010.12.005

  • [29] V. K. Bardsiri, D. N. A. Jawawi, S. Z. M. Hashim, and E. Khatibi, “A PSO-Based Model to Increase the Accuracy of Software Development Effort Estimation,” Software Quality Journal, vol. 21, no. 3, pp. 501–526, Sep. 2012. https://doi.org/10.1007/s11219-012-9183-x

  • [30] V. K. Bardsiri, D. N. A. Jawawi, S. Z. M. Hashim, and E. Khatibi, “Increasing the Accuracy of Software Development Effort Estimation Using Projects Clustering,” IET software, vol. 6, no. 6, pp. 461–473, Dec. 2012. https://doi.org/10.1049/iet-sen.2011.0210

  • [31] A. K. Bardsiri, S. M. Hashemi, and M. Razzazi, “Statistical analysis of the most popular software service effort estimation datasets,” Journal of Telecommunication, Electronic and Computer Engineering, vol. 7, no. 1, pp. 87–96, 2015.

  • [32] D. E. Goldberg and J. Richardson, “Genetic Algorithms With Sharing for Multimodal Function Optimization” in Proceedings of the Second International Conference on Genetic Algorithms, Lawrence Erlbaum, 1987.

  • [33] D. L. Davies and D. W. Bouldin, “A Cluster Separation Measure,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 1, no. 2, pp. 224–227, Apr. 1979. https://doi.org/10.1109/TPAMI.1979.4766909

  • [34] C.-H. Chou, M.-C. Su, and E. Lai, “A New Cluster Validity Measure and Its Application to Image Compression,” Pattern Analysis and Applications, vol. 7, no. 2, pp. 205–220, Jun. 2004. https://doi.org/10.1007/s10044-004-0218-1

  • [35] E. Atashpaz-Gargari and C. Lucas, “Imperialist Competitive Algorithm: An Algorithm for Optimization Inspired by Imperialistic Competition” in 2007 IEEE Congress on Evolutionary Computation, IEEE, 2007, pp. 4661–4667. https://doi.org/10.1109/CEC.2007.4425083

  • [36] B. W. Boehm, “Software Engineering Economics,” IEEE Transactions on Software Engineering, vol. 10, no. 1, pp. 4–21, Jan. 1984. https://doi.org/10.1109/TSE.1984.5010193

  • [37] A. J. Albrecht and J. E. Gaffney, “Software Function, Source Lines of Code, and Development Effort Prediction: A Software Science Validation,” IEEE Transactions on Software Engineering, vol. 9, no. 6, pp. 639–648, Nov. 1983. https://doi.org/10.1109/TSE.1983.235271

  • [38] J. M. Desharnais, “Analyse statistique de la productivitie des projets informatique a partie de la technique des point des fonction,” Master’s Thesis, University of Montreal, 1989.

  • [39] K. D. Maxwell, Applied Statistics for Software Managers, Prentice Hall, 2002.

  • [40] International Software Benchmarking Standards Group. [Online]. Available: https://www.isbsg.org/


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