International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Survey of Cost Estimating Software Development Using Machine Learning

Abstract

The estimate of software project costs and efforts is a critical step. Meeting a plethora of diverse criteria, such as resource allocation, cost estimate, effort estimation, time estimation, and the shifting expectations of software customers, is a component of software estimating. Software Engineering Models assist project managers in estimating the cost, delivery time, and personnel that were crucial for software development. SDCE (Software Development Cost Estimation) has long been an exciting and developing area. There are several intelligent models used to predict or estimate the cost of software. The aim of this research is to highlight the algorithms that are used for cost estimation. However, there are no models that work in different circumstances that each practitioner or researcher chooses there are models that use algorithms and those that don't. This makes it easier to determine how much it will cost to build software. Comparing machine learning approaches to traditional software estimating, the ability to anticipate program expense with a high rate of accuracy is possible.

Country : Iraq

1 Nedaa Thamer Qassem2 Ibrahim Ahmed Saleh

  1. Student, Department of Software, College of Computer & Math., University of Mosul, Iraq
  2. Professor, Department of Software, College of Computer & Math., University of Mosul, Iraq

IRJIET, Volume 7, Issue 12, December 2023 pp. 67-72

doi.org/10.47001/IRJIET/2023.712009

Full Paper
Download

References

  1. Ilham C. S., Riyanarto S., Sholiq, Implementation of Random Forest Regression for COCOMO II Effort Estimation. IEEE reg, 2020.
  2. I.Attarzadeh, A. Mehranzadeh, and A. Barati, “Proposing an Enhanced Artificial Neural Network Prediction Model to Improve the Accuracy in Software Effort Estimation,” in 2012 Fourth International Conference on Computational Intelligence, Communication Systems and Networks, Phuket, Thailand, Jul. 2012, pp. 167–172, doi: 10.1109/CICSyN.2012.39.
  3. S.-J. Huang and N.-H. Chiu, “Applying fuzzy neural network to estimate software development effort,” Appl Intell, vol. 30, no. 2, pp. 73–83, Apr. 2009, doi: 10.1007/s10489- 007-0097-4.
  4. A.B. Nassif, L. F. Capretz, and D. Ho, “Estimating Software Effort Based on Use Case Point Model Using Sugeno Fuzzy Inference System,” in 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence, Boca Raton, FL, USA, Nov. 2011, pp. 393–398, doi: 10.1109/ICTAI.2011.64.
  5. Mahdi, M.N.; Mohamed Zabil, M.H.; Ahmad, A.R.; Ismail, R.; Yusoff, Y.; Cheng, L.K.; Azmi, M.S.B.M.; Natiq, H.; Happala Naidu, H. Software Project Management Using Machine Learning Technique—A Review. Appl. Sci. 2021.
  6. Shweta.KR, Dr. S.Duraisamy, Dr. T.Latha Maheswari,  Software Cost and Effort Estimation using Ensemble Duck Traveler Optimization Algorithm (eDTO) in Earlier Stage,  Turkish Journal of Computer and Mathematics Education Vol.12 No.13 (2021), 3300-3311, 4 June 2021.
  7. Junaid Rashid, Muhammad Wasif Nisar, Toqeer Mahmood, Amjad Rehman, Syed Yasser Arafat, A Study of Software Development Cost Estimation Techniques and Models, Mehran University Research Journal of Engineering and Technology, Vol. 39, No. 2, 413- 431, April 2020.
  8. Noor Azura Zakaria, Amelia Ritahani Ismail, Afrujaan Yakath AliNur Hidayah Mohd Khalid, Nadzurah Zainal Abidin, Software Project Estimation with Machine Learning, Vol. 12, No. 6, 2021.
  9. A G, P.V.; K, A.K.; Varadarajan, V. Estimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach. Electronics 2021.
  10. Sakineh Asghari Agcheh Dizaj, Farhad Soleimanian Gharehchopogh, A New Approach to Software Cost Estimation by Improving Genetic Algorithm with Bat Algorithm, 18 September 2018.
  11. A.Saberi Nejad, R. Tavoli,  A Method for Estimating the Cost of Software Using Principle Components Analysis and Data Mining, 20 December 2017.
  12. Reddy, P. V. G. D., et al. "Software effort estimation using radial basis and generalized regression neural networks." arXiv preprint arXiv:1005.4021 (2010).‏
  13. Dave, Vachik S., and Kamlesh Dutta. "Neural network based models for software effort estimation: a review." Artificial Intelligence Review 42.2 (2014): 295-307.
  14. Hidmi, Omar, and BetulErdogdu Sakar. "Software development effort estimation using ensemble machine learning." Int. J. Comput. Commun. Instrum. Eng 4.1 (2017): 143-147.‏
  15. Saljoughinejad, Ramin, and Vahid Khatibi. "A new optimized hybrid model based on COCOMO to increase the accuracy of software cost estimation." Journal of Advances in Computer Engineering and Technology 4.1 (2018): 27-40.
  16. Arslan, Farrukh. "A review of machine learning models for software cost estimation." Review of Computer Engineering Research 6.2 (2019): 64-75.
  17. Khazaiepoor, Mahdi, Amid Khatibi Bardsiri, and Farshid Keynia. "A hybrid approach for software development effort estimation using neural networks, genetic algorithm, multiple linear regression and imperialist competitive algorithm." International Journal of Nonlinear Analysis and Applications 11.1 (2020): 207-224.
  18. Abdulmajeed, Ashraf Abdulmunim, Marwa Adeeb Al-Jawaherry, and Tawfeeq Mokdad Tawfeeq. "Predict the required cost to develop Software Engineering projects by Using Machine Learning." Journal of Physics: Conference Series. Vol. 1897. No. 1. IOP Publishing, 2021.‏
  19. Ahmad, Farah B., and Laheeb M. Ibrahim. "Software Development Effort Estimation Techniques Using Long Short Term Memory." 2022 International Conference on Computer Science and Software Engineering (CSASE). IEEE, 2022.‏
  20. Şengüneş, Burcu, and Nursel Öztürk. "An Artificial Neural Network Model for Project Effort Estimation." Systems 11.2 (2023): 91.

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies