Optimized COCOMO parameters using hybrid particle swarm optimization

(1) Noor Azura Zakaria Mail (Department of Computer Science, International Islamic University Malaysia, Malaysia)
(2) * Amelia Ritahani Ismail Mail (Department of Computer Science, International Islamic University Malaysia, Malaysia)
(3) Nadzurah Zainal Abidin Mail (Department of Computer Science, International Islamic University Malaysia, Malaysia)
(4) Nur Hidayah Mohd Khalid Mail (Department of Computer Science, International Islamic University Malaysia, Malaysia)
(5) Afrujaan Yakath Ali Mail (Department of Computer Science, International Islamic University Malaysia, Malaysia)
*corresponding author


Software effort and cost estimation are crucial parts of software project development. It determines the budget, time, and resources needed to develop a software project. The success of a software project development depends mainly on the accuracy of software effort and cost estimation. A poor estimation will impact the result, which worsens the project management. Various software effort estimation model has been introduced to resolve this problem. COnstructive COst MOdel (COCOMO) is a well-established software project estimation model; however, it lacks accuracy in effort and cost estimation, especially for current projects. Inaccuracy and complexity in the estimated effort have made it difficult to efficiently and effectively develop software, affecting the schedule, cost, and uncertain estimation directly. In this paper, Particle Swarm Optimization (PSO) is proposed as a metaheuristics optimization method to hybrid with three traditional state-of-art techniques such as Support Vector Machine (SVM), Linear Regression (LR), and Random Forest (RF) for optimizing the parameters of COCOMO models. The proposed approach is applied to the NASA software project dataset downloaded from the promise repository. Comparing the proposed approach has been made with the three traditional algorithms; however, the obtained results confirm low accuracy before hybrid with PSO. Overall, the results showed that PSOSVM on the NASA software project dataset could improve effort estimation accuracy and outperform other models.




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[1] S. Sabbagh Jafari and F. Ziaaddini, "Optimization of software cost estimation using harmony search algorithm," 1st Conf. Swarm Intell. Evol. Comput. CSIEC 2016 - Proc., pp. 131–135, 2016, doi: 10.1109/CSIEC.2016.7482119.

[2] 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. 1, pp. 1–16, 2020, doi: 10.1142/S1469026820500054.

[3] O. Hidmi and B. E. Sakar, "Software Development Effort Estimation Using Ensemble Machine Learning," Int. J. Comput. Commun. Instrum. Eng., vol. 4, no. 1, 2017, doi: 10.15242/ijccie.e0317026.

[4] A. Kumar, B. D. . Patro, and B. K. Singh, "Parameter Tuning for Software Effort Estimation Using Particle Swarm Optimization Algorithm," Int. J. Appl. Eng. Res., vol. 14, no. 2, pp. 139–144, 2019. Available at: Google Scholar.

[5] 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, 2013, doi: 10.1007/s11219-012-9183-x.

[6] S. K. Sehra, Y. S. Brar, N. Kaur, and G. Kaur, "Optimization of COCOMO Parameters using TLBO Algorithm," Int. J. Comput. Intell. Res., vol. 13, no. 4, pp. 525–535, 2017. Available at: Google Scholar.

[7] S. Basha and D. Ponnurangam, "Analysis of Empirical Software Effort Estimation Models," Int. J. Comput. Sci. Inf. Secur., vol. 7, no. 3, pp. 68–77, 2010. Availabel at: Google Scholar.

[8] S. Hajar Arbain, N. Azizah Ali, and N. Haszlinna Mustaffa, "Adoption of Machine Learning Techniques in Software Effort Estimation: An Overview," IOP Conf. Ser. Mater. Sci. Eng., vol. 551, no. 1, 2019, doi: 10.1088/1757-899X/551/1/012074.

[9] S. Ardiansyah, A., Mardhia, M. M., & Handayaningsih, “Analogy-based model for software project effort estimation,” Int. J. Adv. Intell. Informatics, vol. 4, no. 3, pp. 251–260, 2018, doi: 10.26555/ijain.v4i3.266.

[10] G.-H. Cho, H.-G., Kim, K.-G., Kim, J.-Y., & Kim, "A Comparison of Construction Cost Estimation Using Multiple Regression Analysis and Neural Network in Elementary School Project.," J. Korea Inst. Build. Constr., vol. 13, no. 1, pp. 66–74, 2013, doi: 10.5345/jkibc.2013.13.1.066.

[11] A. Banimustafa, "Predicting Software Effort Estimation Using Machine Learning Techniques," 8th Int. Conf. Comput. Sci. Inf. Technol. CSIT 2018, (October), pp. 249–256, 2018, doi: 10.1109/CSIT.2018.8486222.

[12] K. Langsari and R. Sarno, "Optimizing effort and time parameters of COCOMO II estimation using fuzzy multi-objective PSO," Int. Conf. Electr. Eng. Comput. Sci. Informatics, vol. 2017-Decem, no. September, pp. 19–21, 2017, doi: 10.1109/EECSI.2017.8239157.

[13] R. Kalaivani, N., & Beena, "Overview of Software Defect Prediction Using Machine Learning Algorithms," Int. J. Pure Appl. Math., vol. 118, pp. 3863–3873, 2018. Available at: Google Scholar.

[14] J. Thomas, "The Science of Uncertainty: Blown Budgets and Destroyed Schedules. Sometimes, It's Weak Project Estimation That's to Blame," pp. 56–61, 2019. Available at: https://www.pmi.org/

[15] R. M. H. Ghatasheh, N., Faris, H., Aljarah, I., & 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.

[16] M. Z. Alsaeedi, A., & Khan, "Software Defect Prediction Using Supervised Machine Learning and Ensemble Techniques: A Comparative Study," J. Softw. Eng. Appl., vol. 12, no. 05, pp. 85–100, 2019, doi: 10.4236/jsea.2019.125007.

[17] Rekha, "Effort Estimation Using ML Models," vol. 6, no. 1, pp. 1–2, 2017.

[18] V. K. Bhatia, S., & Attri, "Machine Learning Techniques in Software Effort Estimation Using COCOMO Dataset," vol. 2, no. 6, pp. 101–106, 2015. Available at: Google Scholar.

[19] H. D. Delaney and A. Vargha, "A Critique and Improvement of the CL Common Language Effect Size Statistics of McGraw and Wong," J. Educ. Behav. Stat., vol. 25, no. 2, pp. 101–132, 2000. doi: 10.3102/10769986025002101.

[20] M. Parwita, R. Sarno, and A. Puspaningrum, "Optimization of COCOMO II Coefficients using Cuckoo Optimization Algorithm to Improve The Accuracy of Effort Estimation," Int. Conf. Inf. Commun. Technol. Syst., pp. 99–104, 2017. doi: 10.1109/ICTS.2017.8265653

[21] D. Nandal and O. P. Sangwan, "Software cost estimation by optimizing COCOMO model using hybrid BATGSA algorithm," Int. J. Intell. Eng. Syst., vol. 11, no. 4, pp. 250–263, 2018, doi: 10.22266/ijies2018.0831.25.

[22] A. Baghe, M. Rathod, and P. Singh, "Software Effort Estimation using parameter tuned Models," 2020. Available at: Google Scholar.

[23] C. E. Carbonera, K. Farias, and V. Bischoff, "Software development effort estimation: A systematic mapping study," IET Softw., vol. 14, no. 4, pp. 328–344, 2020, doi: 10.1049/iet-sen.2018.5334.

[24] R. Saljoughinejad and V. Khatibi, "A new optimized hybrid model based on COCOMO to increase the accuracy of software cost estimation," J. Adv. Comput. Eng. Technol., vol. 4, no. 1, pp. 27–40, 2018. Available at: Google Scholar.

[25] L. Radlinski and W. Hoffmann, "On Predicting Software Development Effort using Machine Learning Techniques and Local Data," Int. J. Softw. Eng. Comput., vol. 2, no. 2, pp. 123–136, 2010. Available at: Google Scholar.

[26] F. Nayebi, A. Abran, and J.-M. Desharnais, "Automated selection of a software effort estimation model based on accuracy and uncertainty," Artif. Intell. Res., vol. 4, no. 2, 2015, doi: 10.5430/air.v4n2p45.

[27] 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.

[28] R. K. Sachan et al., "Optimizing Basic COCOMO Model Using Simplified Genetic Algorithm," Procedia Comput. Sci., vol. 89, pp. 492–498, 2016, doi: 10.1016/j.procs.2016.06.107.

[29] A. Khatoon and R. Kaur, "Optimization Estimation Parameters of COCOMO Model II Through Genetic Algorithm," Int. J. Comput. Sci. Eng., vol. 6, no. 5, pp. 221–226, 2018, doi: 10.26438/ijcse/v6i5.221226.

[30] B. W. Boehm et al., Software Cost Estimation with COCOMO II. Upper Saddle River, NJ: Prentice Hall, 2000. Available at: Google Scholar.

[31] I. C. Suherman, R. Sarno, and Sholiq, “Implementation of random forest regression for COCOMO II effort estimation,” Proc. - 2020 Int. Semin. Appl. Technol. Inf. Commun. IT Challenges Sustain. Scalability, Secur. Age Digit. Disruption, iSemantic 2020, pp. 476–481, 2020, doi: 10.1109/iSemantic50169.2020.9234269.

[32] S. Asija, "Software Engineering | COCOMO Model," Geeks for Geeks, 2017.

[33] N. A. Samat et al., "A Study of Data Imputation Using Fuzzy C-Means with Particle Swarm Optimization," Recent Adv. Soft Comput. Data Min., vol. 549, no. January, 2017, doi: 10.1007/978-3-319-51281-5.

[34] D. Wang, D. Tan, and L. Liu, "Particle swarm optimization algorithm : an overview," Soft Comput., vol. 22, no. 2, pp. 387–408, 2018, doi: 10.1007/s00500-016-2474-6.

[35] J. Mercieca and S. G. Fabri, "A Metaheuristic Particle Swarm Optimization Approach to Nonlinear Model Predictive Control," Int. J. Adv. Intell. Syst., vol. 5, no. 3, pp. 357–369, 2012. Available at: Google Scholar.

[36] M. Imran, R. Hashim, N. Elaiza, A. Khalid, and H. Onn, "An Overview of Particle Swarm Optimization Variants," Procedia Eng., vol. 53, no. 1, pp. 491–496, 2013, doi: 10.1016/j.proeng.2013.02.063.

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