Analogy-based model for software project effort estimation

(1) * Ardiansyah Ardiansyah Mail (Universitas Ahmad Dahlan, Indonesia)
(2) Murein Miksa Mardhia Mail (Universitas Ahmad Dahlan, Indonesia)
(3) Sri Handayaningsih Mail (Universitas Ahmad Dahlan, Indonesia)
*corresponding author

Abstract


Accurate effort estimation of software development plays an important role to predict how much effort should be prepared during the works of a software project so that it can be completed on time and budget. Some sectors, e.g. banking sectors, were renowned fields of software projects, not only due to its huge size of project, but also extremely expensive and takes a long time to completion. Project estimation is essential for software development project able to run on time and budget with maximum quality. This study aims to investigate the accuracy of software project effort estimation with the Analogy method using three parameters: Euclidean, Manhattan and Minkowski distance. Analogy based estimation consists several stage included similarity measure, analogy adaptation, estimation calculation and model evaluation. The results showed that the best combination of Analogy methods was using Manhattan distance with an accuracy of 50% MMRE, 28% MdMRE and Pred(25) 48%. Thus, we can concluded that this model can be used to predict accurately.

Keywords


Analogy estimation; Effort estimation; Software project; Similarity distance; Machine learning

   

DOI

https://doi.org/10.26555/ijain.v4i3.266
      

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References


[1] A. Dennis, B. H. Wixom, and D. Tegarden, Systems analysis and design: An object-oriented approach with UML, 5th ed. Wiley, 2015, available at: Google Scholar.

[2] M. Cohn, Agile estimating and planning. Prentice Hall, 2006, available at: Google Scholar.

[3] A. J. Albrecht and J. E. Gaffney, “Software Function, Source Lines of Code, and Development Effort Prediction: A Software Science Validation,” IEEE Trans. Softw. Eng., vol. SE-9, no. 6, pp. 639–648, Nov. 1983, doi: https://doi.org/10.1109/TSE.1983.235271.

[4] B. W. Boehm et al., Software Cost Estimation with Cocomo II with Cdrom, 1st ed. Upper Saddle River, NJ, USA: Prentice Hall PTR, 2000, available at: https://dl.acm.org/citation.cfm?id=557000.

[5] L. Laird and M. C. Brennan, Software Measurement and Estimation: A Practical Approach. Wiley, 2006, doi: https://doi.org/10.1002/0471792535.

[6] C. F. Kemerer, “An empirical validation of software cost estimation models,” Commun. ACM, vol. 30, no. 5, pp. 416-429, 1987, doi: https://doi.org/10.1145/22899.22906.

[7] Y. Miyazaki, M. Terakado, K. Ozaki, and H. Nozaki, “Robust regression for developing software estimation models,” J. Syst. Softw., vol. 27, no. 1, pp. 3–16, Oct. 1994, doi: https://doi.org/10.1016/0164-1212(94)90110-4.

[8] F. Yücalar, D. Kilinc, E. Borandag, and A. Ozcift, “Regression Analysis Based Software Effort Estimation Method,” Int. J. Softw. Eng. Knowl. Eng., vol. 26, no. 05, pp. 807–826, Jun. 2016, doi: https://doi.org/10.1142/S0218194016500261.

[9] A. Trendowicz and R. Jeffery, Software Project Effort Estimation: Foundation and Best Practice Guidelines for Success. Springer, 2014, doi: https://doi.org/10.1007/978-3-319-03629-8.

[10] F. Zare, H. Khademi Zare, and M. S. Fallahnezhad, “Software effort estimation based on the optimal Bayesian belief network,” Appl. Soft Comput., vol. 49, pp. 968–980, 2016, doi: https://doi.org/10.1016/j.asoc.2016.08.004.

[11] C. . Dawson, “A Neural Network Approach to Software Project Effort Estimation,” Int. Conf. Appl. Artif. Intell. Eng., vol. 16, no. 9, 1996, doi: https://doi.org/ 10.2495/AI960161.

[12] Q. M. Yousef, Y. A. Alshaer, and N. K. Alhammad, “Dragonfly Estimator: A Hybrid Software Projects’ Efforts Estimation Model using Artificial Neural Network and Dragonfly Algorithm,” Int. J. Comput. Sci. Netw. Secur., vol. 17, no. 9, pp. 108–120, 2017, available at: http://paper.ijcsns.org/07_book/201709/20170916.pdf.

[13] P. L. Braga, A. L. I. Oliveira, and S. R. L. Meira, “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 - SAC ’08, 2008, p. 1788, doi: https://doi.org/10.1145/1363686.1364116.

[14] S.-J. Huang, C.-Y. Lin, and N.-H. Chiu, “Fuzzy Decision Tree Approach for Embedding Risk Assessment Information into Software Cost Estimation Model,” J. Inf. Sci. Eng., vol. 22, pp. 297–313, 2006, available at: https://pdfs.semanticscholar.org/7a1a/34bc97e67a9253debdc7b5b5e9f3ec149b52.pdf.

[15] M. Choetkiertikul, H. K. Dam, T. Tran, T. T. M. Pham, A. Ghose, and T. Menzies, “A deep learning model for estimating story points,” IEEE Trans. Softw. Eng., pp. 1–1, 2018, doi: https://doi.org/10.1109/TSE.2018.2792473.

[16] E. Kocaguneli, T. Menzies, and J. W. Keung, “On the Value of Ensemble Effort Estimation,” IEEE Trans. Softw. Eng., vol. 38, no. 6, pp. 1403–1416, Nov. 2012, doi: https://doi.org/10.1109/TSE.2011.111.

[17] C. López-Martín and A. Abran, “Neural networks for predicting the duration of new software projects,” J. Syst. Softw., vol. 101, pp. 127–135, 2015, doi: https://doi.org/10.1016/j.jss.2014.12.002.

[18] A. Idri, F. A. Amazal, and A. Abran, “Analogy-based software development effort estimation: A systematic mapping and review,” Inf. Softw. Technol., vol. 58, pp. 206–230, 2015, doi: https://doi.org/10.1016/j.infsof.2014.07.013.

[19] D. J. Reifer, B. Boehm, and S. Chulani, “The Rosetta Stone: Making COCOMO 81 Estimates Work with COCOMO II,” Crosstalk. J. Def. Softw. Eng., pp. 11–15, 1999, available at: http://sunset.usc.edu/TECHRPTS/1998/usccse98-516/usccse98-516.pdf.

[20] I. Limited, “isbsg10,” Nov-2012, doi: https://doi.org/10.5281/zenodo.268485.

[21] Y. Li, “Effort Estimation: Maxwell,” Mar-2009, doi: https://doi.org/10.5281/zenodo.268461.

[22] J. W. Keung, “kemerer,” Apr-2010, doi: https://doi.org/10.5281/zenodo.268464.

[23] I. Limited, “Effort Estimation: Cosmic,” Nov-2012, doi: https://doi.org/10.5281/zenodo.268482.

[24] N.-H. Chiu and S.-J. Huang, “The adjusted analogy-based software effort estimation based on similarity distances,” J. Syst. Softw., vol. 80, no. 4, pp. 628–640, Apr. 2007, doi: https://doi.org/10.1016/j.jss.2006.06.006.

[25] M. Shepperd and C. Schofield, “Estimating software project effort using analogies,” IEEE Trans. Softw. Eng., vol. 23, no. 11, pp. 736–743, Nov. 1997, doi: https://doi.org/10.1109/32.637387.

[26] E. Mendes, Cost Estimation Techniques for Web Projects. IGI Global, 2008, doi: https://doi.org/10.4018/978-1-59904-135-3.

[27] M. Azzeh, A. B. Nassif, and L. L. Minku, “An empirical evaluation of ensemble adjustment methods for analogy-based effort estimation,” J. Syst. Softw., vol. 103, pp. 36–52, May 2015, doi: https://doi.org/10.1016/j.jss.2015.01.028.

[28] M. Hosni and A. Idri, “Software effort estimation using classical analogy ensembles based on random subspace,” in Proceedings of the Symposium on Applied Computing, 2017, pp. 1251–1258, doi: https://doi.org/10.1145/3019612.3019784.

[29] S. Mensah, J. Keung, M. Bosu, K. E. Bennin, and P. K. Kudjo, “A Stratification and Sampling Model for Bellwether Moving Window,” 2017, pp. 481–486, available at: http://ksiresearchorg.ipage.com/seke/seke17paper/seke17paper_126.pdf.

[30] Y. F. Li, M. Xie, and T. N. Goh, “A study of mutual information based feature selection for case based reasoning in software cost estimation,” Expert Syst. Appl., vol. 36, no. 3, pp. 5921–5931, Apr. 2009, doi: https://doi.org/10.1016/j.eswa.2008.07.062.

[31] M. Tsunoda, T. Kakimoto, A. Monden, and K. Matsumoto, “An empirical evaluation of outlier deletion methods for analogy-based cost estimation,” in Proceedings of the 7th International Conference on Predictive Models in Software Engineering, 2011, pp. 1–10, doi: https://doi.org/10.1145/2020390.2020407.




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