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

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

Applied Computer Systems
Volume 24 (2019): Issue 2 (December 2019)
By: Mahdi Khazaiepoor,  Amid Khatibi Bardsiri and  Farshid Keynia  
Open Access
|Feb 2020

References

  1. [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/10.1145/2884781.288482710.1145/2884781.2884827
    Search in Google ScholarBack to article
  2. [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.2017.07.01510.1016/j.infsof.2017.07.015
    Search in Google ScholarBack to article
  3. [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.2016.08.00410.1016/j.asoc.2016.08.004
    Search in Google ScholarBack to article
  4. [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.2013.03.00110.1016/j.infsof.2013.03.001
    Search in Google ScholarBack to article
  5. [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.2005.04.00410.1016/j.infsof.2005.04.004
    Search in Google ScholarBack to article
  6. [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)00021-310.1016/S0164-1212(02)00021-3
    Search in Google ScholarBack to article
  7. [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.2007.25694310.1109/TSE.2007.256943
    Search in Google ScholarBack to article
  8. [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/s00521-015-2127-110.1007/s00521-015-2127-1
    Search in Google ScholarBack to article
  9. [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)00203-810.1016/S0950-5849(01)00203-8
    Search in Google ScholarBack to article
  10. [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_910.1007/978-3-642-27207-3_9
    Search in Google ScholarBack to article
  11. [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/10.1080/18756891.2013.80558310.1080/18756891.2013.805583
    Search in Google ScholarBack to article
  12. [12] D. Eck, et al., Parametric Estimating Handbook, The International Society of Parametric Analysts, 2009.
    Search in Google ScholarBack to article
  13. [13] J. Lynch, “Chaos Manifesto,” The Standish Group, 2009.
    Search in Google ScholarBack to article
  14. [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.2014.10.03210.1016/j.jss.2014.10.032
    Search in Google ScholarBack to article
  15. [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/10.1109/32.29589510.1109/32.295895
    Search in Google ScholarBack to article
  16. [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.2006.11410.1109/TSE.2006.114
    Search in Google ScholarBack to article
  17. [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/s10664-011-9192-610.1007/s10664-011-9192-6
    Search in Google ScholarBack to article
  18. [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.2014.07.01310.1016/j.infsof.2014.07.013
    Search in Google ScholarBack to article
  19. [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/10.1080/02533839.2016.117687310.1080/02533839.2016.1176873
    Search in Google ScholarBack to article
  20. [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/s10515-012-0108-510.1007/s10515-012-0108-5
    Search in Google ScholarBack to article
  21. [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/s11227-010-0525-910.1007/s11227-010-0525-9
    Search in Google ScholarBack to article
  22. [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_004210.1142/9789814261302_0042
    Search in Google ScholarBack to article
  23. [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.2006.118.12310.3844/jcssp.2006.118.123
    Search in Google ScholarBack to article
  24. [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.
    Search in Google ScholarBack to article
  25. [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.2008.463096110.1109/CEC.2008.4630961
    Search in Google ScholarBack to article
  26. [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.2006.06.00610.1016/j.jss.2006.06.006
    Search in Google ScholarBack to article
  27. [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.2005.12.02010.1016/j.infsof.2005.12.020
    Search in Google ScholarBack to article
  28. [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.2010.12.00510.1016/j.eswa.2010.12.005
    Search in Google ScholarBack to article
  29. [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-x10.1007/s11219-012-9183-x
    Search in Google ScholarBack to article
  30. [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.2011.021010.1049/iet-sen.2011.0210
    Search in Google ScholarBack to article
  31. [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.
    Search in Google ScholarBack to article
  32. [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.
    Search in Google ScholarBack to article
  33. [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.1979.476690910.1109/TPAMI.1979.4766909
    Search in Google ScholarBack to article
  34. [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/s10044-004-0218-110.1007/s10044-004-0218-1
    Search in Google ScholarBack to article
  35. [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.2007.442508310.1109/CEC.2007.4425083
    Search in Google ScholarBack to article
  36. [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.1984.501019310.1109/TSE.1984.5010193
    Search in Google ScholarBack to article
  37. [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.1983.23527110.1109/TSE.1983.235271
    Search in Google ScholarBack to article
  38. [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.
    Search in Google ScholarBack to article
  39. [39] K. D. Maxwell, Applied Statistics for Software Managers, Prentice Hall, 2002.
    Search in Google ScholarBack to article
  40. [40] International Software Benchmarking Standards Group. [Online]. Available: https://www.isbsg.org/
    Search in Google ScholarBack to article
DOI: https://doi.org/10.2478/acss-2019-0011 | Journal eISSN: 2255-8691 | Journal ISSN: 2255-8683
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Language: English
Page range: 82 - 93
Published on: Feb 20, 2020
Published by: Riga Technical University
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
Clustering,
estimation,
feature selection,
genetic algorithm,
imperialist competitive algorithm,
neural network,
regression,
software development effort
Related subjects:
Computer sciences,
Artificial intelligence,
Information technology,
Project management,
Software development

© 2020 Mahdi Khazaiepoor, Amid Khatibi Bardsiri, Farshid Keynia, published by Riga Technical University
This work is licensed under the Creative Commons Attribution 4.0 License.

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