(Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta |
Department of Informatics, Faculty of Industrial Technology, Universitas Ahmad Dahlan, Yogyakarta, Indonesia)(2) Ridi Ferdiana
(Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia)(3) Adhistya Erna Permanasari
(Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia)*corresponding author
AbstractAmong algorithmic-based frameworks for software development effort estimation, Use Case Points I s one of the most used. Use Case Points is a well-known estimation framework designed mainly for object-oriented projects. Use Case Points uses the use case complexity weight as its essential parameter. The parameter is calculated with the number of actors and transactions of the use case. Nevertheless, use case complexity weight is discontinuous, which can sometimes result in inaccurate measurements and abrupt classification of the use case. The objective of this work is to investigate the potential of integrating particle swarm optimization (PSO) with the Use Case Points framework. The optimizer algorithm is utilized to optimize the modified use case complexity weight parameter. We designed and conducted an experiment based on real-life data set from three software houses. The proposed model’s accuracy and performance evaluation metric is compared with other published results, which are standardized accuracy, effect size, mean balanced residual error, mean inverted balanced residual error, and mean absolute error. Moreover, the existing models as the benchmark are polynomial regression, multiple linear regression, weighted case-based reasoning with (PSO), fuzzy use case points, and standard Use Case Points. Experimental results show that the proposed model generates the best value of standardized accuracy of 99.27% and an effect size of 1.15 over the benchmark models. The results of our study are promising for researchers and practitioners because the proposed model is actually estimating, not guessing, and generating meaningful estimation with statistically and practically significant.
KeywordsUse case points; Effort estimation; Particle swarm; Metaheuristic optimizationUse case complexity; optimization;
|
DOIhttps://doi.org/10.26555/ijain.v8i2.811 |
Article metricsAbstract views : 1015 | PDF views : 232 |
Cite |
Full Text Download
|
References
[1] M. Choetkiertikul, H. K. Dam, T. Tran, A. Ghose, and J. Grundy, “Predicting Delivery Capability in Iterative Software Development,” IEEE Trans. Softw. Eng., vol. 44, no. 6, pp. 551–573, Jun. 2018, doi: 10.1109/TSE.2017.2693989.
[2] M. Bloch, S. Blumberg, and J. Laartz, “Delivering large-scale IT projects on time, on budget, and on value,” McKinsey Digital, no. 5. pp. 1–7, 2012. Available at: Google Scholar
[3] A. Kaur and K. Kaur, “A COSMIC function points based test effort estimation model for mobile applications,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 3, pp. 946–963, Mar. 2022, doi: 10.1016/j.jksuci.2019.03.001.
[4] K. Rak, Ž. Car, and I. Lovrek, “Effort estimation model for software development projects based on use case reuse,” J. Softw. Evol. Process, vol. 31, no. 2, pp. 1–17, Feb. 2019, doi: 10.1002/smr.2119.
[5] G. Karner, “Resource Estimation for Objectory Projects.” pp. 1–9, 1993. Available at: Google Scholar
[6] Y. Xie, J. Guo, and A. Shen, “Use Case Points Method of Software Size Measurement Based on Fuzzy Inference,” in Proceedings of the 4th International Conference on Computer Engineering and Networks, vol. 355, W. E. Wong, Ed. Cham: Springer International Publishing, 2015, pp. 11–18. doi: 10.1007/978-3-319-11104-9_2
[7] F. Wang, X. Yang, X. Zhu, and L. Chen, “Extended Use Case Points Method for Software Cost Estimation,” in 2009 International Conference on Computational Intelligence and Software Engineering, 2009, pp. 1–5, doi: 10.1109/CISE.2009.5364706.
[8] A. B. Nassif, L. F. Capretz, and D. Ho, “Calibrating use case points,” in Companion Proceedings of the 36th International Conference on Software Engineering - ICSE Companion 2014, 2014, pp. 612–613, doi: 10.1145/2591062.2591141.
[9] M. Hariyanto and R. S. Wahono, “Estimasi Proyek Pengembangan Perangkat Lunak Dengan Fuzzy Use Case Points,” J. Softw. Eng., vol. 1, no. 1, pp. 54–63, 2015. Available at: Google Scholar
[10] A. B. Nassif, L. F. Capretz, and D. Ho, “Enhancing Use Case Points Estimation Method Using Soft Computing Techniques,” J. Glob. Res. Comput. Sci., vol. 1, no. 4, pp. 12–21, Dec. 2016. Available at: Google Scholar
[11] V. Khatibi 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,” Softw. Qual. J., vol. 21, no. 3, pp. 501–526, Sep. 2013, doi: 10.1007/s11219-012-9183-x.
[12] M. Azzeh, A. B. Nassif, S. Banitaan, and F. Almasalha, “Pareto efficient multi-objective optimization for local tuning of analogy-based estimation,” Neural Comput. Appl., vol. 27, no. 8, pp. 2241–2265, Nov. 2016, doi: 10.1007/s00521-015-2004-y.
[13] D. Wu, J. Li, and C. Bao, “Case-based reasoning with optimized weight derived by particle swarm optimization for software effort estimation,” Soft Comput., vol. 22, no. 16, pp. 5299–5310, Aug. 2018, doi: 10.1007/s00500-017-2985-9.
[14] L. Brezočnik, I. Fister, and V. Podgorelec, “Solving Agile Software Development Problems with Swarm Intelligence Algorithms,” in Lecture Notes in Networks and Systems, vol. 76, E. Karabegovi, Ed. Sarajevo, Bosnia and Herzegovina: Springer, Cham, 2020, pp. 298–309. doi: 10.1007/978-3-030-18072-0_35
[15] F. Ferrucci, C. Gravino, R. Oliveto, and F. Sarro, “Genetic Programming for Effort Estimation: An Analysis of the Impact of Different Fitness Functions,” in 2nd International Symposium on Search Based Software Engineering, 2010, no. 25, pp. 89–98, doi: 10.1109/SSBSE.2010.20.
[16] J. Murillo-Morera, C. Quesada-López, C. Castro-Herrera, and M. Jenkins, “A genetic algorithm based framework for software effort prediction,” J. Softw. Eng. Res. Dev., vol. 5, no. 1, pp. 1–33, Dec. 2017, doi: 10.1186/s40411-017-0037-x.
[17] Z. Shahpar, V. K. Bardsiri, and A. K. Bardsiri, “Polynomial analogy‐based software development effort estimation using combined particle swarm optimization and simulated annealing,” Concurr. Comput. Pract. Exp., vol. 33, no. 20, p. e6358, Oct. 2021, doi: 10.1002/cpe.6358.
[18] P. Phannachitta, “On an optimal analogy-based software effort estimation,” Inf. Softw. Technol., vol. 125, no. April, p. 106330, Sep. 2020, doi: 10.1016/j.infsof.2020.106330.
[19] Z. Shahpar, V. Khatibi, and A. Khatibi Bardsiri, “Hybrid PSO-SA Approach for Feature Weighting in Analogy-Based Software Project Effort Estimation,” J. AI Data Min., vol. 9, no. 3, pp. 329–340, 2021, doi: 10.22044/jadm.2021.10119.2152.
[20] Z. Shahpar, V. K. Bardsiri, and A. K. Bardsiri, “An evolutionary ensemble analogy‐based software effort estimation,” Softw. Pract. Exp., vol. 52, no. 4, pp. 929–946, Apr. 2022, doi: 10.1002/spe.3040.
[21] T. R. Benala and R. Mall, “DABE: Differential evolution in analogy-based software development effort estimation,” Swarm Evol. Comput., vol. 38, pp. 158–172, Feb. 2018, doi: 10.1016/j.swevo.2017.07.009.
[22] N. Ghatasheh, H. Faris, I. Aljarah, and R. M. H. Al-Sayyed, “Optimizing Software Effort Estimation Models Using Firefly Algorithm,” J. Softw. Eng. Appl., vol. 08, no. 03, pp. 133–142, 2015, doi: 10.4236/jsea.2015.83014.
[23] V. Resmi, S. Vijayalakshmi, and R. S. Chandrabose, “An effective software project effort estimation system using optimal firefly algorithm,” Cluster Comput., vol. 22, no. S5, pp. 11329–11338, Sep. 2019, doi: 10.1007/s10586-017-1388-0.
[24] K. Langsari and R. Sarno, “Optimizing effort and time parameters of COCOMO II estimation using fuzzy multi-objective PSO,” in 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2017, vol. 2017-Decem, no. September, pp. 1–6, doi: 10.1109/EECSI.2017.8239157.
[25] N. A. Zakaria, A. R. Ismail, N. Z. Abidin, N. H. M. Khalid, and A. Y. Ali, “Optimized COCOMO parameters using hybrid particle swarm optimization,” Int. J. Adv. Intell. Informatics, vol. 7, no. 2, pp. 177–187, Apr. 2021, doi: 10.26555/ijain.v7i2.583.
[26] M. D. Alanis-Tamez, C. López-Martín, and Y. Villuendas-Rey, “Particle Swarm Optimization for Predicting the Development Effort of Software Projects,” Mathematics, vol. 8, no. 10, pp. 1–21, Oct. 2020, doi: 10.3390/math8101819.
[27] S. Chhabra and H. Singh, “Optimizing Design of Fuzzy Model for Software Cost Estimation Using Particle Swarm Optimization Algorithm,” Int. J. Comput. Intell. Appl., vol. 19, no. 01, pp. 1–16, Mar. 2020, doi: 10.1142/S1469026820500054.
[28] M. Khazaiepoor, A. Khatibi Bardsiri, and F. Keynia, “A Hybrid Approach for Software Development Effort Estimation using Neural networks, Genetic Algorithm, Multiple Linear Regression and Imperialist Competitive Algorithm,” Int. J. Nonlinear Anal. Appl, vol. 11, no. 1, pp. 207–224, 2020, doi: 10.22075/ijnaa.2020.4259.
[29] K. E. Rao and G. A. Rao, “Ensemble learning with recursive feature elimination integrated software effort estimation: a novel approach,” Evol. Intell., vol. 14, no. 1, pp. 151–162, Mar. 2021, doi: 10.1007/s12065-020-00360-5.
[30] S. P. Singh, G. Dhiman, P. Tiwari, and R. H. Jhaveri, “A soft computing based multi-objective optimization approach for automatic prediction of software cost models,” Appl. Soft Comput., vol. 113, p. 107981, Dec. 2021, doi: 10.1016/j.asoc.2021.107981.
[31] A. Kaushik, S. Verma, H. J. Singh, and G. Chhabra, “Software cost optimization integrating fuzzy system and COA-Cuckoo optimization algorithm,” Int. J. Syst. Assur. Eng. Manag., vol. 8, no. S2, pp. 1461–1471, Nov. 2017, doi: 10.1007/s13198-017-0615-7.
[32] S. Kumari and S. Pushkar, “Cuckoo search based hybrid models for improving the accuracy of software effort estimation,” Microsyst. Technol., vol. 24, no. 12, pp. 4767–4774, Dec. 2018, doi: 10.1007/s00542-018-3871-9.
[33] M. R. Braz and S. R. Vergilio, “Using fuzzy theory for effort estimation of object-oriented software,” in 16th IEEE International Conference on Tools with Artificial Intelligence, 2004, no. Ictai, pp. 196–201, doi: 10.1109/ICTAI.2004.119.
[34] A. Ardiansyah, R. Ferdiana, and A. E. Permanasari, “MUCPSO: A Modified Chaotic Particle Swarm Optimization with Uniform Initialization for Optimizing Software Effort Estimation,” Appl. Sci., vol. 12, no. 3, p. 1081, Jan. 2022, doi: 10.3390/app12031081.
[35] G. Robiolo and R. Orosco, “Employing use cases to early estimate effort with simpler metrics,” Innov. Syst. Softw. Eng., vol. 4, no. 1, pp. 31–43, Apr. 2008, doi: 10.1007/s11334-007-0043-y.
[36] P. Mohagheghi, B. Anda, and R. Conradi, “Effort estimation of use cases for incremental large-scale software development,” in Proceedings. 27th International Conference on Software Engineering, 2005. ICSE 2005., 2005, pp. 303–311, doi: 10.1109/ICSE.2005.1553573.
[37] B. Anda, H. Dreiem, D. I. K. Sjøberg, and M. Jørgensen, “Estimating Software Development Effort Based on Use Cases — Experiences from Industry,” in International Conference on the Unified Modeling Language, 2001, pp. 487–502. doi: 10.1007/3-540-45441-1_35
[38] B. Anda, E. Angelvik, and K. Ribu, “Improving Estimation Practices by Applying Use Case Models,” in Product Focused Software Process Improvement, vol. 2559, no. 1325, 2002, pp. 383–397. doi: 10.1007/3-540-36209-6_32
[39] M. Ochodek, J. Nawrocki, and K. Kwarciak, “Simplifying effort estimation based on Use Case Points,” Inf. Softw. Technol., vol. 53, no. 3, pp. 200–213, Mar. 2011, doi: 10.1016/j.infsof.2010.10.005.
[40] M. Ochodek, B. Alchimowicz, J. Jurkiewicz, and J. Nawrocki, “Improving the reliability of transaction identification in use cases,” Inf. Softw. Technol., vol. 53, no. 8, pp. 885–897, Aug. 2011, doi: 10.1016/j.infsof.2011.02.004.
[41] H. L. T. K. Nhung, V. Van Hai, R. Silhavy, Z. Prokopova, and P. Silhavy, “Parametric Software Effort Estimation Based on Optimizing Correction Factors and Multiple Linear Regression,” IEEE Access, vol. 10, pp. 2963–2986, 2022, doi: 10.1109/ACCESS.2021.3139183.
[42] A. B. Nassif, “Towards an Early Software Estimation Using Log-Linear Regression and a Multilayer Perceptron Model,” J. Syst. Softw., vol. 86, no. 1, pp. 144–160, 2013. doi: 10.1016/j.jss.2012.07.050
[43] M. Azzeh and A. B. Nassif, “A hybrid model for estimating software project effort from Use Case Points,” Appl. Soft Comput., vol. 49, pp. 981–989, Dec. 2016, doi: 10.1016/j.asoc.2016.05.008.
[44] A. B. Nassif, L. F. Capretz, D. Ho, and M. Azzeh, “A Treeboost Model for Software Effort Estimation Based on Use Case Points,” in 2012 11th International Conference on Machine Learning and Applications, 2012, no. December 2012, pp. 314–319, doi: 10.1109/ICMLA.2012.155.
[45] K. Qi, A. Hira, E. Venson, and B. W. Boehm, “Calibrating use case points using bayesian analysis,” in Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, 2018, pp. 1–10, doi: 10.1145/3239235.3239236.
[46] R. Silhavy, P. Silhavy, and Z. Prokopova, “Using Actors and Use Cases for Software Size Estimation,” Electronics, vol. 10, no. 5, pp. 1–20, Mar. 2021, doi: 10.3390/electronics10050592.
[47] H. Le Thi Kim Nhung, H. T. Hoc, and V. Van Hai, “An Evaluation of Technical and Environmental Complexity Factors for Improving Use Case Points Estimation,” in Advances in Intelligent Systems and Computing, vol. 1294, 2020, pp. 757–768. doi: 10.1007/978-3-030-63322-6_64
[48] K. Qi and B. W. Boehm, “Detailed use case points (DUCPs),” in Proceedings of the 10th International Workshop on Modelling in Software Engineering - MiSE ’18, 2018, pp. 17–24, doi: 10.1145/3193954.3193955.
[49] D. D. Galorath and M. W. Evans, Software Sizing, Estimation, and Risk Management. Boca Raton: Auerbach Publications, 2006. doi: 10.1201/9781420013122
[50] K. Periyasamy and A. Ghode, “Cost Estimation Using Extended Use Case Point (e-UCP) Model,” in 2009 International Conference on Computational Intelligence and Software Engineering, 2009, pp. 1–5, doi: 10.1109/CISE.2009.5364515.
[51] M. Manzoor and A. Wahid, “Revised Use Case Point (Re-UCP) Model for Software Effort Estimation,” Int. J. Adv. Comput. Sci. Appl., vol. 6, no. 3, pp. 65–71, 2015, doi: 10.14569/IJACSA.2015.060310.
[52] A. Minkiewicz, “Use Case Sizing,” in International Forum on COCOMO and Software Cost Modelin, 2004. Available at: Google Scholar
[53] A. B. Nassif, “Software Size and Effort Estimation from Use Case Diagrams Using Regression and Soft Computing Models,” The University of Western Ontario, 2012. Available at: Google Scholar
[54] H. T. Hoc, V. Van Hai, and H. Le Thi Kim Nhung, “AdamOptimizer for the Optimisation of Use Case Points Estimation,” in Advances in Intelligent Systems and Computing, vol. 1294, 2020, pp. 747–756. doi: 10.1007/978-3-030-63322-6_63
[55] Sholiq, A. P. Subriadi, F. A. Muqtadiroh, and R. S. Dewi, “A model of owner estimate cost for software development project in Indonesia,” J. Softw. Evol. Process, vol. 31, no. 10, p. e2175, Oct. 2019, doi: 10.1002/smr.2175.
[56] Subriadi, A. Pribadi, and P. A. Ningrum, “Critical Review of the Effort Rate Value in Use Case Point Method for Estimating Software Development Effort,” vol. 59, no. 3, pp. 735–744, 2014. Available at: Google Scholar
[57] M. Azzeh and A. B. Nassif, “Project productivity evaluation in early software effort estimation,” J. Softw. Evol. Process, vol. 30, no. 12, p. e2110, Dec. 2018, doi: 10.1002/smr.2110.
[58] R. Silhavy, P. Silhavy, and Z. Prokopova, “Analysis and selection of a regression model for the Use Case Points method using a stepwise approach,” J. Syst. Softw., vol. 125, pp. 1–14, Mar. 2017, doi: 10.1016/j.jss.2016.11.029.
[59] A. Ali and C. Gravino, “An empirical comparison of validation methods for software prediction models,” J. Softw. Evol. Process, vol. 33, no. 8, pp. 1–38, Aug. 2021, doi: 10.1002/smr.2367.
[60] E. Alpaydin, Introduction to machine learning. Massachusetts: MIT press, 2014. Available at: Google Books
[61] E. Kocaguneli and T. Menzies, “Software effort models should be assessed via leave-one-out validation,” J. Syst. Softw., vol. 86, no. 7, pp. 1879–1890, Jul. 2013, doi: 10.1016/j.jss.2013.02.053.
[62] Q. Li, Q. Wang, Y. Yang, and M. Li, “Reducing biases in individual software effort estimations,” in Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement - ESEM ’08, 2008, pp. 223–232, doi: 10.1145/1414004.1414041.
[63] Ardiansyah, R. Ferdiana, and A. E. Permanasari, “Use Case Points based software effort prediction using regression analysis,” in 2019 International Conference on Advanced Computer Science and information Systems (ICACSIS), 2019, pp. 15–20, doi: 10.1109/ICACSIS47736.2019.8979851.
[64] M. Shepperd and S. MacDonell, “Evaluating prediction systems in software project estimation,” Inf. Softw. Technol., vol. 54, no. 8, pp. 820–827, Aug. 2012, doi: 10.1016/j.infsof.2011.12.008.
[65] P. D. Ellis, The Essential Guide to Effect Sizes. Cambridge: Cambridge University Press, 2010. doi: 10.1017/CBO9780511761676
[66] J. Cohen, “A power primer.,” Psychol. Bull., vol. 112, no. 1, pp. 155–159, 1992, doi: 10.1037/0033-2909.112.1.155.
[67] A. P. Piotrowski, J. J. Napiorkowski, and A. E. Piotrowska, “Population size in Particle Swarm Optimization,” Swarm Evol. Comput., vol. 58, no. May, p. 100718, Nov. 2020, doi: 10.1016/j.swevo.2020.100718.
[68] Y. Shi and R. C. Eberhart, “Empirical study of particle swarm optimization,” in Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), 1999, vol. 3, pp. 1945–1950, doi: 10.1109/CEC.1999.785511.
[69] I. D. Kenestie and Sholiq, “Determining Effort Rate (ER) Value for Use Case Points based Educational Software Development Effort Estimation,” J. Tek. POMITS, pp. 1–11, 2011. Available at: Google Scholar
[70] Sholiq, T. Sutanto, A. P. Widodo, and W. Kurniawan, “Effort Rate on Use Case Point Method for Effort Estimation of Website Development,” J. Theor. Appl. Inf. Technol., vol. 63, no. 1, pp. 209–218, 2014. Available at: Google Scholar
[71] R. Silhavy, P. Silhavy, and Z. Prokopova, “Evaluating subset selection methods for use case points estimation,” Inf. Softw. Technol., vol. 97, no. June 2017, pp. 1–9, May 2018, doi: 10.1016/j.infsof.2017.12.009.
[72] A. B. Nassif, D. Ho, and L. F. Capretz, “Towards an early software estimation using log-linear regression and a multilayer perceptron model,” J. Syst. Softw., vol. 86, no. 1, pp. 144–160, Jan. 2013, doi: 10.1016/j.jss.2012.07.050.
[73] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95 - International Conference on Neural Networks, 1995, vol. 4, no. 2, pp. 1942–1948, doi: 10.1109/ICNN.1995.488968.
[74] E.-G. Talbi, Metaheuristics: From Design to Implementation. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2009. Available at: Google Scholar
[75] M. Harman, “The Current State and Future of Search Based Software Engineering,” in Future of Software Engineering (FOSE ’07), 2007, pp. 342–357, doi: 10.1109/FOSE.2007.29.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
___________________________________________________________
International Journal of Advances in Intelligent Informatics
ISSN 2442-6571 (print) | 2548-3161 (online)
Organized by UAD and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W: http://ijain.org
E: info@ijain.org (paper handling issues)
andri.pranolo.id@ieee.org (publication issues)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0