Predict software project completion time and cost using XGBoost
15 viewsDOI:
https://doi.org/10.54939/1859-1043.j.mst.94.2024.149-158Keywords:
Software project management; EVM; XGBoost.Abstract
Nowadays, with the rapid development of information technology, managing costs and time to complete software projects has become an urgent issue. To be able to manage software projects, the need to predict costs and completion times is extremely important. Traditional methods often use EVM earned value management to predict project costs and completion times. However, this method often does not achieve very high accuracy when the data has a lot of noise. In recent years, machine learning methods have emerged as a useful solution for leveraging past data to predict future values. In this study, we propose to use the XGBoost machine learning model to predict project costs and completion time. Experimental results show that XGBoost has the potential to solve this problem.
References
[1]. Simion, Cezar-Petre, and Irinel Marin. “Project cost estimate at completion: earmed value management versus earned schedule-based regression models. A comparative analysis of the models application in the construction projects in Romania”. Economic Computation & Economic Cybernetics Studies & Research 52.3, (2018). DOI: https://doi.org/10.24818/18423264/52.3.18.14
[2]. Nannini, G., R.D.H, Warburton, and A. De Marco. “Improving the accuracy of project estimates at completion using the Gompertz function”. International Research Network on Organizing by Projects (IRNOP), UTS ePRESS, Sydney: NSW, pp.1-15, (2017). DOI: https://doi.org/10.5130/pmrp.irnop2017.5670
[3]. Narbaev T .; De Marco A. “Combination of Growth Model and Earned Schedule to Forecast Project Cost at Completion”. In: Journal of Construction engineering and management, vol. 140 n. 1, Article number 04013038-. - ISSN 0733-9364, (2014). DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000783
[4]. Batselier, J., & Vanhoucke, M. “Evaluation of deterministic state-of-the-art forecasting approaches for project duration based on earning value management”. International Journal of Project Management, 33 (7), 1588-1596, (2015). DOI: https://doi.org/10.1016/j.ijproman.2015.04.003
[5]. Khamooshi, H., Golafshani, H. EDM: “Earned Duration Management, a new approach to schedule performance management and measurement”. Int. J. Proj. Manag. 32, 1019–1041, (2014). DOI: https://doi.org/10.1016/j.ijproman.2013.11.002
[6]. Quentin WeFleming, Joel MeKoppelman. “Earned Value Project Management”. Project Management Institute Newtown Square, Pennsylvania USA, (2015).
[7]. Elshaer, R. “Impact of sensitivity information on the prediction of project's duration using earning schedule method”. International Journal of Project Management, 31 (4), 579-588, (2013). DOI: https://doi.org/10.1016/j.ijproman.2012.10.006
[8]. Mukherjee, I., & Routroy, S. “Comparing the performance of neural networks developed by using Levenberg – Marquardt and Quasi-Newton with the gradient descent algorithm for modeling a multiple response grinding process”. Expert Systems with Applications, 39 (3), 2397-2407, (2012). DOI: https://doi.org/10.1016/j.eswa.2011.08.087
[9]. Bottou, L. “Large-scale machine learning with stochastic gradient descent”. In Proceedings of COMPSTAT'2010 (pp. 177-186). Physica-Verlag HD, (2010). DOI: https://doi.org/10.1007/978-3-7908-2604-3_16
[10]. D. Dong, and T. McAvoy, “Nonlinear principal component analysis — based on principal curves and neural networks”, Computers & Chemical Engineering, vol. 20, no. 1, pp. 65-78, (1996). DOI: https://doi.org/10.1016/0098-1354(95)00003-K
[11]. R. S. FAN, Y. LI, and T. T. MA, “Research and application of project settlement overdue prediction based on xgboost intelligent algorithm”, in Sustainable Power and Energy Conference (iSPEC), IEEE, pp. 1213-1216, (2019).
[12]. H. H. Elmousalami, "Comparison of artificial intelligence techniques for project conceptual cost prediction: A case study and comparative analysis", IEEE Transactions on Engineering Management, vol. 68, no. 1, pp. 183-196, (2021). DOI: https://doi.org/10.1109/TEM.2020.2972078
[13]. YAN, Hongyan, et al, “Investment estimation of prefabricated concrete buildings based on XGBoost machine learning algorithm”, Advanced Engineering Informatics, vol 54, no. c: 101789, (2022). DOI: https://doi.org/10.1016/j.aei.2022.101789
[14]. L. T. Anh, N. T. Hung, H. Q. Thang, and N. V. Can, “Calibrating the future performance factor PF in the EVM-GM method of evaluating software project completion: testing and evaluation”, in National Conference XXI: Some selected issues of Information and Communications Technology, Thanh Hoa, Vietnam, pp. 137-143, (2018).
[15]. Batselier, J., Vanhoucke, M., available at:
http://www.projectmanagement.ugeưnt.be/research/data/realdata
[16]. Ruder, Sebastian, “An overview of gradient descent optimization algorithms”, (2016).
