A Study of Evaluation Measures for Software Effort Estimation Using Machine Learning


  • Rajani Kumari Gora Computer Science S. S. Jain Subodh P.G. College, Rajasthan Technical University, Kota
  • Ripu Ranjan Sinha Computer Science S. S. Jain Subodh P.G. College, Rajasthan Technical University, Kota


Software development, Effort estimation, Software development life cycle (SDLC), SDLC models, Machine learning


Software effort estimation is a crucial process which involves predicting how much time and money will be needed to accomplish a software development project. Expert opinion and past data are used in conventional estimating techniques, which may be inefficient and prone to mistakes. Machine learning offers a promising approach to automate this process by learning from past projects and predicting effort estimates for new projects. Using machine learning, this article takes an in-depth look at the practise of software work estimation. In this study, several machine learning models, including support vector machine, KNN, ANN, linear regression, support vector machine, and neural network, are trained and evaluated on a dataset of software projects. This paper also presents some comparative results of the various machine learning algorithms, including multilayer perceptron (MLP), support vector machine (SVM), and linear regression (LR), showing that the MLP model achieves the lowest MMRE value, 13%, while the SVM achieves the highest PRED(25), 87.65%, for software effort estimation.


Download data is not yet available.


J. Wen, S. Li, Z. Lin, Y. Hu, and C. Huang, “Systematic literature review of machine learning based software development effort estimation models,” Information and Software Technology. 2012, doi: 10.1016/j.infsof.2011.09.002.

S. M. Satapathy, B. P. Acharya, and S. K. Rath, “Early stage software effort estimation using random forest technique based on use case points,” IET Softw., 2016, doi: 10.1049/iet-sen.2014.0122.

Z. Dan, “Improving the accuracy in software effort estimation: Using artificial neural network model based on particle swarm optimization,” 2013, doi: 10.1109/SOLI.2013.6611406.

Tung Khuat and Hanh Le, “ An Effort Estimation Approach for Agile Software Development using Fireworks Algorithm Optimized Neural Network.,” Int. J. Comput. Sci. Inf. Secur., 2016.

M. N. Mahdi et al., “Software Project Management Using Machine Learning Technique—A Review,” Appl. Sci., vol. 11, no. 11, 2021, doi: 10.3390/app11115183.

A. T. Raslan and N. R. Darwish, “An enhanced framework for effort estimation of agile projects,” Int. J. Intell. Eng. Syst., 2018, doi: 10.22266/IJIES2018.0630.22.

A. A. Abdulmajeed, M. A. Al-Jawaherry, and T. M. Tawfeeq, “Predict the required cost to develop Software Engineering projects by Using Machine Learning,” 2021, doi: 10.1088/1742-6596/1897/1/012029.

N. A. Mohamed, A. Al-Qasmi, S. Al-Lamki, M. Bayoumi, and A. Al-Hinai, “An estimation of staffing requirements in primary care in Oman using the workload indicators of staffing needs method,” East. Mediterr. Heal. J., 2018, doi: 10.26719/2018.24.9.823.

E. Meenakshi and E. Sumeet, “Review on Software Effort estimation by machine learning Approaches,” vol. 9028, pp. 2002–2005, 2018.

J. G. Borade and V. R. Khalkar, “Software Project Effort and Cost Estimation Techniques,” Int. J. Adv. Res. Comput. Sci. Softw. Eng., 2013.

Rekha T. and R. P.K., “Machine Learning Methods of Effort Estimation and It’s Performance Evaluation Criteria,” Int. J. Comput. Sci. Mob. Comput., 2017.

R. Malhotra and A. Jain, “Software Effort Prediction using Statistical and Machine Learning Methods,” Int. J. Adv. Comput. Sci. Appl., 2011, doi: 10.14569/ijacsa.2011.020122.

P. Seema Suresh Kute and P. Surabhi Deependra Thorat, “A Review on Various Software Development Life Cycle ( SDLC ) Models,” Int. J. Res. Comput. Commun. Technol., 2017.

A. M. Kale, V. V Bandal, and K. Chaudhari, “A Review Paper on Software Testing,” Int. Res. J. Eng. Technol., vol. 5, no. 2, p. 1268, 2008, [Online]. Available: www.irjet.net.

G. Gurung, R. Shah, and D. P. Jaiswal, “Software Development Life Cycle Models-A Comparative Study,” Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., no. July 2020, pp. 30–37, 2020, doi: 10.32628/cseit206410.

B. Tarika, “A Review of Software Development Life Cycle Models,” Int. J. Adv. Res. Comput. Sci. Softw. Eng., vol. 3, pp. 62–68, 2019, doi: 10.31058/j.data.2019.34002.

M. M. Moore, E. Slonimsky, A. D. Long, R. W. Sze, and R. S. Iyer, “Machine learning concepts, concerns and opportunities for a pediatric radiologist,” Pediatr. Radiol., 2019, doi: 10.1007/s00247-018-4277-7.

G. Chartrand et al., “Deep learning: A primer for radiologists,” Radiographics. 2017, doi: 10.1148/rg.2017170077.

A. Akella and S. Akella, “Machine learning algorithms for predicting coronary artery disease: Efforts toward an open source solution,” Futur. Sci. OA, 2021, doi: 10.2144/fsoa-2020-0206.

A. Kanneganti, “Using Ensemble Machine Learning Methods in Estimating Software Development Effort,” no. October, 2020.

F. Pedregosa et al., “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res., 2011.

M. Humayun and C. Gang, “Estimating Effort in Global Software Development Projects Using Machine Learning Techniques,” Int. J. Inf. Educ. Technol., 2012, doi: 10.7763/ijiet.2012.v2.111.

I. H. Sarker, “Machine Learning: Algorithms, Real-World Applications and Research Directions,” SN Comput. Sci., 2021, doi: 10.1007/s42979-021-00592-x.

I. H. Sarker, “A machine learning based robust prediction model for real-life mobile phone data,” Internet of Things (Netherlands), 2019, doi: 10.1016/j.iot.2019.01.007.

Ritu and Y. Garg, “Comparative Analysis of Machine Learning Techniques in Effort Estimation,” in 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON), 2022, vol. 1, pp. 401–405, doi: 10.1109/COM-IT-CON54601.2022.9850592.

Y. Assefa, F. Berhanu, A. Tilahun, and E. Alemneh, “Software Effort Estimation using Machine learning Algorithm,” in 2022 International Conference on Information and Communication Technology for Development for Africa (ICT4DA), 2022, pp. 163–168, doi: 10.1109/ICT4DA56482.2022.9971209.

P. Brar and D. Nandal, “A Systematic Literature Review of Machine Learning Techniques for Software Effort Estimation Models,” in 2022 Fifth International Conference on Computational Intelligence and Communication Technologies (CCICT), 2022, pp. 494–499, doi: 10.1109/CCiCT56684.2022.00093.

N. Govil and A. Sharma, “Estimation of cost and development effort in Scrum-based software projects considering dimensional success factors,” Adv. Eng. Softw., vol. 172, p. 103209, 2022, doi: https://doi.org/10.1016/j.advengsoft.2022.103209.

H.-C. Jang and S.-C. Wu, “Tracking of Hardware Development Schedule based on Software Effort Estimation,” in 2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2022, pp. 305–310, doi: 10.1109/IEMCON56893.2022.9946524.

A. Setiadi, W. F. Hidayat, A. Sinnun, A. Setiawan, M. Faisal, and D. P. Alamsyah, “Analyze the Datasets of Software Effort Estimation With Particle Swarm Optimization,” in 2021 International Seminar on Intelligent Technology and Its Applications (ISITIA), 2021, pp. 197–201, doi: 10.1109/ISITIA52817.2021.9502208.

and Z. J. Israr ur Rehman, Zulfiqar Ali, “An Empirical Analysis on Software Development Efforts Estimation in Machine Learning Perspective,” ADCAIJ Adv. Distrib. Comput. Artif. Intell., vol. 10, 2021.

N. Saini and B. Khalid, “Empirical Evaluation of machine learning techniques for software effort estimation,” IOSR J. Comput. Eng., 2014, doi: 10.9790/0661-16193438.

H. M. Premalatha and C. V. Srikrishna, “Effort estimation in agile software development using evolutionary cost- sensitive deep Belief Network,” Int. J. Intell. Eng. Syst., vol. 12, no. 2, pp. 261–269, 2019, doi: 10.22266/IJIES2019.0430.25.

M. Vyas and N. Hemrajani, “Predicting effort of agile software projects using linear regression, ridge regression and logistic regression,” Int. J. Tech. Phys. Probl. Eng., 2021.

B. Marapelli*, “Software Development Effort Duration and Cost Estimation using Linear Regression and K-Nearest Neighbors Machine Learning Algorithms,” Int. J. Innov. Technol. Explor. Eng., 2019, doi: 10.35940/ijitee.k2306.129219.

Alaria, S. K. "A.. Raj, V. Sharma, and V. Kumar.“Simulation and Analysis of Hand Gesture Recognition for Indian Sign Language Using CNN”." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 4 (2022): 10-14.

Satish Kumar Alaria. Design & Analysis of Cost Estimation for New Mobile-COCOMO Tool for Mobile Application. IJRITCC 2019, 7, 27-34.

Najneen Qureshi, Manish Kumar Mukhija and Satish Kumar, "RAFI: Parallel Dynamic Test-suite Reduction for Software", New Frontiers in Communication and Intelligent Systems, SCRS, India, 2021, pp. 165-176. https://doi.org/10.52458/978-81-95502-00-4-20.

Type of software estimation process




How to Cite

R. K. . Gora and R. R. . Sinha, “A Study of Evaluation Measures for Software Effort Estimation Using Machine Learning ”, Int J Intell Syst Appl Eng, vol. 11, no. 6s, pp. 267–275, May 2023.



Research Article