Vol. 6 No. 2 (2025)

Standard Journal Issues

Software Effort Estimation Using Stacking Ensemble and Bayesian Optimization

Published 2025-08-07

  • Muhammad Yusuf
    • ,
  • Daniel Siahaan

Muhammad Yusuf
Department Informatics Engineering, Institut Teknologi Sepuluh Nopember, Surabaya Indonesia, Indonesia

Daniel Siahaan
Department Informatics Engineering, Institut Teknologi Sepuluh Nopember,Surabaya Indonesia, Indonesia

Abstract

Accurately estimating software costs is a vital step in ensuring the successful completion of a software project. There is a need for estimation techniques that ensure projects are completed on time, within budget, and with the desired quality. Accurate estimation plays a crucial role in crafting realistic budget plans and ensuring that projects are completed on time with sufficient resources. When estimations are precise, teams can spot potential issues early, distribute resources more effectively, and handle risks with greater confidence. This research focuses on boosting the reliability of software effort estimation by applying a stacking method enhanced with Bayesian hyperparameter optimization. It leverages three core algorithms: SVM, Random Forest, and Decision Tree, each fine-tuned using the proposed approach. Evaluations across 11 public datasets reveal noteworthy improvements, ranging from 0.2 to 0.5. A significance test confirms the model’s strong performance, showing a p-value greater than 0.5, which indicates that the results are statistically meaningful. These findings suggest that combining stacking with Bayesian tuning holds promise for refining software effort predictions. It can serve as a valuable reference for future project planning across diverse modeling approaches.

Keywords

Software Estimation, Ensemble Learning, Stacking, Hyperparameter Tuning, Bayesian Optimization, Machine Learning, Model Performance.

PDF

References

  1. J. Leong, K. May Yee, O. Baitsegi, L. Palanisamy, and R. K. Ramasamy, “Hybrid Project Management between Traditional Software Development Lifecycle and Agile Based Product Development for Future Sustainability,” Sustainability, vol. 15, no. 2, p. 1121, Jan. 2023, doi: 10.3390/su15021121.
  2. P. G. F. Matsubara, B. F. Gadelha, I. Steinmacher, and T. U. Conte, “SEXTAMT: A systematic map to navigate the wide seas of factors affecting expert judgment software estimates,” Journal of Systems and Software, vol. 185, p. 111148, Mar. 2022, doi: 10.1016/j.jss.2021.111148.
  3. A.-E. Iordan, “An Optimized LSTM Neural Network for Accurate Estimation of Software Development Effort,” Mathematics, vol. 12, no. 2, p. 200, Jan. 2024, doi: 10.3390/math12020200.
  4. K. Rathor, J. Kaur, U. A. Nayak, S. Kaliappan, R. Maranan, and V. Kalpana, “Technological Evaluation and Software Bug Training using Genetic Algorithm and Time Convolution Neural Network (GA-TCN),” in 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), Trichy, India: IEEE, Aug. 2023, pp. 7–12. doi: 10.1109/ICAISS58487.2023.10250760.
  5. R. K. B. N and Y. Suresh, “Software Effort Estimation using ANN (Back Propagation),” in 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India: IEEE, Feb. 2023, pp. 1–2. doi: 10.1109/ICCMC56507.2023.10084264.
  6. Prisca Amajuoyi, Lucky Bamidele Benjamin, and Kudirat Bukola Adeusi, “Optimizing agile project management methodologies in high-tech software development,” GSC Adv. Res. Rev., vol. 19, no. 2, pp. 268–274, May 2024, doi: 10.30574/gscarr.2024.19.2.0182.
  7. B. Kapulica and K. Jurina, “Application of artificial intelligence in project management: analysis of potentials and challenges,” Et2er, vol. 6, no. 2, pp. 115–121, Dec. 2024, doi: 10.70077/et2er.6.2.15.
  8. Ritu and P. Bhambri, “Enhancing software development effort estimation with a cloud-based data framework using use case points, fuzzy logic, and machine learning,” Discov Computing, vol. 28, no. 1, p. 143, Jul. 2025, doi: 10.1007/s10791-025-09668-1.
  9. K. S. Thant and H. H. Khaung Tin, “LEARNING THE EFFICIENT ESTIMATION TECHNIQUES FOR SUCCESSFUL SOFTWARE PROJECT MANAGEMENT,” Innovare J Eng & Tech, pp. 4–8, May 2023, doi: 10.22159/ijet.2023.v11i1.47605.
  10. B. A. Almahameed and M. Bisharah, “Applying Machine Learning and Particle Swarm Optimization for predictive modeling and cost optimization in construction project management,” Asian J Civ Eng, vol. 25, no. 2, pp. 1281–1294, Feb. 2024, doi: 10.1007/s42107-023-00843-7.
  11. F. Sarro, R. Moussa, A. Petrozziello, and M. Harman, “Learning From Mistakes: Machine Learning Enhanced Human Expert Effort Estimates,” IIEEE Trans. Software Eng., vol. 48, no. 6, pp. 1868–1882, Jun. 2022, doi: 10.1109/TSE.2020.3040793.
  12. M. Fernandez-Diego, E. R. Mendez, F. Gonzalez-Ladron-De-Guevara, S. Abrahao, and E. Insfran, “An Update on Effort Estimation in Agile Software Development: A Systematic Literature Review,” IEEE Access, vol. 8, pp. 166768–166800, 2020, doi: 10.1109/ACCESS.2020.3021664.
  13. A. Bahi, J. Gharib, and Y. Gahi, “Integrating Generative AI for Advancing Agile Software Development and Mitigating Project Management Challenges,” IJACSA, vol. 15, no. 3, 2024, doi: 10.14569/IJACSA.2024.0150306.
  14. A. Mahardika and A. Retnowardhani, “Effectiveness Agile Project Management Tools For Managing Software Development Using F-AHP Method,” in 2024 7th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), Yogyakarta, Indonesia: IEEE, Dec. 2024, pp. 860–865. doi: 10.1109/ISRITI64779.2024.10963563.
  15. J. O. Afape et al., “Improving millimetre-wave path loss estimation using automated hyperparameter-tuned stacking ensemble regression machine learning,” Results in Engineering, vol. 22, p. 102289, Jun. 2024, doi: 10.1016/j.rineng.2024.102289.
  16. J. Chen, J. Xu, S. Cai, X. Wang, H. Chen, and Z. Li, “Software Defect Prediction Approach Based on a Diversity Ensemble Combined With Neural Network,” IEEE Trans. Rel., vol. 73, no. 3, pp. 1487–1501, Sep. 2024, doi: 10.1109/TR.2024.3356515.
  17. B. R. P. Damoto et al., “Deep Learning and Ensemble Approaches to Misinformation Detection in Digital News: A Systematic Review,” in 2024 IEEE 10th Information Technology International Seminar (ITIS), Surabaya, Indonesia: IEEE, Nov. 2024, pp. 151–156. doi: 10.1109/ITIS64716.2024.10845709.
  18. Z. Sakhrawi, A. Sellami, and N. Bouassida, “Software Enhancement Effort Estimation using Machine Learning Regression Methods”.
  19. Meharunnisa, M. Saqlain, M. Abid, M. Awais, and Ž. Stevi?, “Analysis of Software Effort Estimation by Machine Learning Techniques,” ISI, vol. 28, no. 6, Dec. 2023, doi: 10.18280/isi.280602.
  20. D. K. Srivastava, A. K. Sharma, and D. Choudhary, “Software Development Effort Estimation Using Machine Learning Techniques: Multi-linear Regression versus Random Forest,” in 2021 International Conference on Computing, Communication and Green Engineering (CCGE), Pune, India: IEEE, Sep. 2021, pp. 1–5. doi: 10.1109/CCGE50943.2021.9776394.
  21. B. R. Paradisiaca Darnoto, D. Siahaan, and D. Purwitasari, “A Comprehensive Ensemble Deep Learning Method for Identifying Native Advertising in News Articles,” in 2023 IEEE 8th International Conference On Software Engineering and Computer Systems (ICSECS), Penang, Malaysia: IEEE, Aug. 2023, pp. 164–169. doi: 10.1109/ICSECS58457.2023.10256392.
  22. I.-G. Chelaru, “Enhancing the performance of software effort estimation through boosting ensemble learning,” in 2023 25th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), Nancy, France: IEEE, Sep. 2023, pp. 300–307. doi: 10.1109/SYNASC61333.2023.00051.
  23. M. Yusuf, A. Haq, and S. Rochimah, “Integrating Adaptive Sampling with Ensembles Model for Software Defect Prediction,” KINETIK, May 2025, doi: 10.22219/kinetik.v10i2.2191.
  24. M. Hammad and M. Amin, “Assuring Software Reuse Success Using Ensemble MachineLearning Algorithms,” IJCDS, vol. 13, no. 1, pp. 69–81, Jan. 2023, doi: 10.12785/ijcds/130107.
  25. Zainab Rustum Mohsin, “Investigating the Use of an Adaptive Neuro-Fuzzy InferenceSystem in Software Development Effort Estimation,” ijcsm, pp. 18–24, Jul. 2021, doi: 10.52866/ijcsm.2021.02.02.003.
  26. M. G. Meharie, W. J. Mengesha, Z. A. Gariy, and R. N. N. Mutuku, “Application of stacking ensemble machine learning algorithm in predicting the cost of highway construction projects,” ECAM, vol. 29, no. 7, pp. 2836–2853, Aug. 2022, doi: 10.1108/ECAM-02-2020-0128.
  27. H. Cho, Y. Kim, E. Lee, D. Choi, Y. Lee, and W. Rhee, “Basic Enhancement Strategies When Using Bayesian Optimization for Hyperparameter Tuning of Deep Neural Networks,” IEEE Access, vol. 8, pp. 52588–52608, 2020, doi: 10.1109/ACCESS.2020.2981072.
  28. S. Hameed, Y. Elsheikh, and M. Azzeh, “An optimized case-based software project effort estimation using genetic algorithm,” Information and Software Technology, vol. 153, p. 107088, Jan. 2023, doi: 10.1016/j.infsof.2022.107088.
  29. A. Jadhav and S. K. Shandilya, “Reliable machine learning models for estimating effective software development efforts: A comparative analysis,” Journal of Engineering Research, vol. 11, no. 4, pp. 362–376, Dec. 2023, doi: 10.1016/j.jer.2023.100150.
  30. Z. Sakhrawi, T. Labidi, A. Sellami, and N. Bouassida, “A Stacking Ensemble Learning Model for Software Development Cost Estimation”.
  31. A. Kaushik, K. Sheoran, R. Kapur, N. Bhutani, B. Singh, and H. Sharma, “SENSE: software effort estimation using novel stacking ensemble learning,” Innovations Syst Softw Eng, vol. 21, no. 2, pp. 769–785, Jun. 2025, doi: 10.1007/s11334-024-00581-2.
  32. P. Srivastava, N. Srivastava, R. Agarwal, and P. Singh, “NEMAEP: A NOVEL ENSEMBLE MACHINE LEARNING FRAMEWORK FOR ACCURATE EFFORT ESTIMATION IN SOFTWARE PROJECTS,” . Vol., no. 24.
  33. S. Kassaymeh, M. Alweshah, M. A. Al-Betar, A. I. Hammouri, and M. A. Al-Ma’aitah, “Software effort estimation modeling and fully connected artificial neural network optimization using soft computing techniques,” Cluster Comput, vol. 27, no. 1, pp. 737–760, Feb. 2024, doi: 10.1007/s10586-023-03979-y.

Downloads

Download data is not yet available.

Similar Articles

11-20 of 53

You may also start an advanced similarity search for this article.