Classifier Ensembles for Software Development Team Effort Estimation: A Rapid Systematic Literature Review
Resumo
This work presents a systematic literature review (SLR) on the use of classifier ensembles for software team effort estimation. The study analyzes the advantages of homogeneous and heterogeneous ensembles, highlighting how the combination of algorithms improves prediction accuracy. A total of 27 relevant studies were examined, using metrics such as MAE, RMSE, and PRED to assess model performance. The results indicate that heterogeneous ensembles are more effective in environments with high variability, while homogeneous ensembles excel in specific domains. The work suggests the exploration of hybrid metrics and dynamic optimization for future research.Referências
Abnane, I., Idri, A., Chlioui, I., and Abran, A. (2023). Evaluating ensemble imputation in software effort estimation. Empirical Software Engineering, 28(2):56.
Abnane, I., Idri, A., Hosni, M., and Abran, A. (2021). Heterogeneous ensemble imputation for software development effort estimation. In Proceedings of the 17th International Conference on Predictive Models and Data Analytics in Software Engineering, pages 1–10.
Ali, S. S., Ren, J., Zhang, K., Wu, J., and Liu, C. (2023). Heterogeneous ensemble model to optimize software effort estimation accuracy. IEEE Access, 11:27759–27792.
Araújo, D. d. C. (2019). Avaliação de comitês com classificadores tradicionais e profundos para análise de sentimentos. Master’s thesis, Universidade Federal de Pernambuco.
Beesetti, K. K., Bilgaiyan, S., and Mishra, B. S. P. (2023). Software effort estimation through ensembling of base models in machine learning using a voting estimator. International Journal of Advanced Computer Science and Applications, 14(2).
Benaroch, M. and Lyytinen, K. (2023). How much does software complexity matter for maintenance productivity? the link between team instability and diversity. IEEE Transactions on Software Engineering, 49(4):2459–2475.
BN, R. K. and Suresh, Y. (2023). Software effort estimation using ann (back propagation). In 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), pages 1–2. IEEE.
Boehm, B., Abts, C., and Chulani, S. (2000). Software development cost estimation approaches—a survey. Annals of software engineering, 10(1):177–205.
Cabral, J. T. H. d. A. and Oliveira, A. L. (2021). Ensemble effort estimation using dynamic selection. Journal of Systems and Software, 175:110904.
Charmanas, K., Mittas, N., and Angelis, L. (2020). Ensemble software development effort estimation using data envelopment analysis. In Proceedings of the 24th Pan-Hellenic Conference on Informatics, pages 202–207.
Goyal, S. (2022a). Effective software effort estimation using heterogenous stacked ensemble. In 2022 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), volume 1, pages 584–588. IEEE.
Goyal, S. (2022b). Effective software effort estimation using heterogenous stacked ensemble. In 2022 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), volume 1, pages 584–588. IEEE.
Hosni, M. (2024). Comparative analysis of single and ensemble support vector regression methods for software development effort estimation. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, pages 509–516. INSTICC, SciTePress.
Hosni, M., Idri, A., and Abran, A. (2019). Evaluating filter fuzzy analogy homogenous ensembles for software development effort estimation. Journal of Software: Evolution and Process, 31(2):e2117.
Hosni, M., Idri, A., and Abran, A. (2021). On the value of filter feature selection techniques in homogeneous ensembles effort estimation. Journal of Software: Evolution and Process, 33(6):e2343.
Javdani Gandomani, T., Dashti, M., Zulzalil, H., and Sultan, A. B. M. (2024). Enhancing software effort estimation in the analogy-based approach through the combination of regression methods. IEEE Access, 12:152122–152137.
Kitchenham, B. A., Budgen, D., and Brereton, P. (2015). Evidence-based software engineering and systematic reviews, volume 4. CRC press.
Labidi, T. and Sakhrawi, Z. (2023). On the value of parameter tuning in stacking ensemble model for software regression test effort estimation. The Journal of Supercomputing, 79(15):17123–17145.
Mahmood, Y., Kama, N., Azmi, A., and Ali, M. (2020a). Improving estimation accuracy prediction of software development effort: A proposed ensemble model. In 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), pages 1–6.
Mahmood, Y., Kama, N., Azmi, A., and Ali, M. (2020b). Improving estimation accuracy prediction of software development effort: a proposed ensemble model. In 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), pages 1–6. IEEE.
Marapelli, B., Carie, A., and Islam, S. M. (2021). Software effort estimation with use case points using ensemble machine learning models. In 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), pages 1–6. IEEE.
Marco, R., Ahmad, S. S. S., and Ahmad, S. (2022). Bayesian hyperparameter optimization and ensemble learning for machine learning models on software effort estimation. International Journal of Advanced Computer Science and Applications, 13(3).
Nassif, A. B., Ho, D., and Capretz, L. F. (2013). Towards an early software estimation using log-linear regression and a multilayer perceptron model. Journal of Systems and Software, 86(1):144–160.
Nosrati, V. and Rahmani, M. (2023). Hmde-fs: A homogeneous distributed ensemble feature selection framework based on resampling with/without replacement. Concurrency and Computation: Practice and Experience, 35(7):e7613.
Raghu Raman, D., Santhagarooban, D., and Sathyanarayanan, N. (2024). Optimizing software effort estimation accuracy with a machine learning model. In 2024 5th International Conference for Emerging Technology (INCET), pages 1–8.
Rai, P., Kumar, S., and Verma, D. K. (2021). Prediction of software effort in the early stage of software development: a hybrid model. IEEE Canadian Journal of Electrical and Computer Engineering, 44(3):376–383.
Rhmann, W., Pandey, B., and Ansari, G. A. (2022). Software effort estimation using ensemble of hybrid search-based algorithms based on metaheuristic algorithms. Innovations in Systems and Software Engineering, 18(2):309–319.
Sakhrawi, Z., Sellami, A., and Bouassida, N. (2022a). Software enhancement effort estimation using correlation-based feature selection and stacking ensemble method. Cluster Computing, 25(4):2779–2792.
Sakhrawi, Z., Sellami, A., and Bouassida, N. (2022b). Support vector regression for enhancement effort prediction of scrum projects from cosmic functional size. Innovations in Systems and Software Engineering, 18(1):137–153.
Senevirathne, D. S. and Wijayasiriwardhane, T. K. (2020). Extending use-case point-based software effort estimation for open source freelance software development. In 2020 International Research Conference on Smart Computing and Systems Engineering (SCSE), pages 188–194.
Seni, G. and Elder, J. (2010). Ensemble methods in data mining: improving accuracy through combining predictions. Morgan & Claypool Publishers.
Shahpar, Z., Bardsiri, V. K., and Bardsiri, A. K. (2022). An evolutionary ensemble analogy-based software effort estimation. Software: Practice and Experience, 52(4):929–946.
Shukla, S. and Kumar, S. (2023a). Self-adaptive ensemble-based approach for software effort estimation. In 2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), pages 581–592. IEEE.
Shukla, S. and Kumar, S. (2023b). Towards ensemble-based use case point prediction. Software Quality Journal, 31(3):843–864.
Srivastava, D. K., Sharma, A. K., and Choudhary, D. (2021). 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), pages 1–5. IEEE.
Thakur, V. and Dutta, K. (2025). Machine learning based effort estimation models for software development projects related datasets with diverse features. In 2025 2nd International Conference on Computational Intelligence, Communication Technology and Networking (CICTN), pages 807–813.
Venson, E. (2020). The effects of required security on software development effort. In 2020 IEEE/ACM 42nd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), pages 166–169.
Wen, J., Li, S., Lin, Z., Hu, Y., and Huang, C. (2012). Systematic literature review of machine learning based software development effort estimation models. Information and Software Technology, 54(1):41–59.
Abnane, I., Idri, A., Hosni, M., and Abran, A. (2021). Heterogeneous ensemble imputation for software development effort estimation. In Proceedings of the 17th International Conference on Predictive Models and Data Analytics in Software Engineering, pages 1–10.
Ali, S. S., Ren, J., Zhang, K., Wu, J., and Liu, C. (2023). Heterogeneous ensemble model to optimize software effort estimation accuracy. IEEE Access, 11:27759–27792.
Araújo, D. d. C. (2019). Avaliação de comitês com classificadores tradicionais e profundos para análise de sentimentos. Master’s thesis, Universidade Federal de Pernambuco.
Beesetti, K. K., Bilgaiyan, S., and Mishra, B. S. P. (2023). Software effort estimation through ensembling of base models in machine learning using a voting estimator. International Journal of Advanced Computer Science and Applications, 14(2).
Benaroch, M. and Lyytinen, K. (2023). How much does software complexity matter for maintenance productivity? the link between team instability and diversity. IEEE Transactions on Software Engineering, 49(4):2459–2475.
BN, R. K. and Suresh, Y. (2023). Software effort estimation using ann (back propagation). In 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), pages 1–2. IEEE.
Boehm, B., Abts, C., and Chulani, S. (2000). Software development cost estimation approaches—a survey. Annals of software engineering, 10(1):177–205.
Cabral, J. T. H. d. A. and Oliveira, A. L. (2021). Ensemble effort estimation using dynamic selection. Journal of Systems and Software, 175:110904.
Charmanas, K., Mittas, N., and Angelis, L. (2020). Ensemble software development effort estimation using data envelopment analysis. In Proceedings of the 24th Pan-Hellenic Conference on Informatics, pages 202–207.
Goyal, S. (2022a). Effective software effort estimation using heterogenous stacked ensemble. In 2022 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), volume 1, pages 584–588. IEEE.
Goyal, S. (2022b). Effective software effort estimation using heterogenous stacked ensemble. In 2022 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), volume 1, pages 584–588. IEEE.
Hosni, M. (2024). Comparative analysis of single and ensemble support vector regression methods for software development effort estimation. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, pages 509–516. INSTICC, SciTePress.
Hosni, M., Idri, A., and Abran, A. (2019). Evaluating filter fuzzy analogy homogenous ensembles for software development effort estimation. Journal of Software: Evolution and Process, 31(2):e2117.
Hosni, M., Idri, A., and Abran, A. (2021). On the value of filter feature selection techniques in homogeneous ensembles effort estimation. Journal of Software: Evolution and Process, 33(6):e2343.
Javdani Gandomani, T., Dashti, M., Zulzalil, H., and Sultan, A. B. M. (2024). Enhancing software effort estimation in the analogy-based approach through the combination of regression methods. IEEE Access, 12:152122–152137.
Kitchenham, B. A., Budgen, D., and Brereton, P. (2015). Evidence-based software engineering and systematic reviews, volume 4. CRC press.
Labidi, T. and Sakhrawi, Z. (2023). On the value of parameter tuning in stacking ensemble model for software regression test effort estimation. The Journal of Supercomputing, 79(15):17123–17145.
Mahmood, Y., Kama, N., Azmi, A., and Ali, M. (2020a). Improving estimation accuracy prediction of software development effort: A proposed ensemble model. In 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), pages 1–6.
Mahmood, Y., Kama, N., Azmi, A., and Ali, M. (2020b). Improving estimation accuracy prediction of software development effort: a proposed ensemble model. In 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), pages 1–6. IEEE.
Marapelli, B., Carie, A., and Islam, S. M. (2021). Software effort estimation with use case points using ensemble machine learning models. In 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), pages 1–6. IEEE.
Marco, R., Ahmad, S. S. S., and Ahmad, S. (2022). Bayesian hyperparameter optimization and ensemble learning for machine learning models on software effort estimation. International Journal of Advanced Computer Science and Applications, 13(3).
Nassif, A. B., Ho, D., and Capretz, L. F. (2013). Towards an early software estimation using log-linear regression and a multilayer perceptron model. Journal of Systems and Software, 86(1):144–160.
Nosrati, V. and Rahmani, M. (2023). Hmde-fs: A homogeneous distributed ensemble feature selection framework based on resampling with/without replacement. Concurrency and Computation: Practice and Experience, 35(7):e7613.
Raghu Raman, D., Santhagarooban, D., and Sathyanarayanan, N. (2024). Optimizing software effort estimation accuracy with a machine learning model. In 2024 5th International Conference for Emerging Technology (INCET), pages 1–8.
Rai, P., Kumar, S., and Verma, D. K. (2021). Prediction of software effort in the early stage of software development: a hybrid model. IEEE Canadian Journal of Electrical and Computer Engineering, 44(3):376–383.
Rhmann, W., Pandey, B., and Ansari, G. A. (2022). Software effort estimation using ensemble of hybrid search-based algorithms based on metaheuristic algorithms. Innovations in Systems and Software Engineering, 18(2):309–319.
Sakhrawi, Z., Sellami, A., and Bouassida, N. (2022a). Software enhancement effort estimation using correlation-based feature selection and stacking ensemble method. Cluster Computing, 25(4):2779–2792.
Sakhrawi, Z., Sellami, A., and Bouassida, N. (2022b). Support vector regression for enhancement effort prediction of scrum projects from cosmic functional size. Innovations in Systems and Software Engineering, 18(1):137–153.
Senevirathne, D. S. and Wijayasiriwardhane, T. K. (2020). Extending use-case point-based software effort estimation for open source freelance software development. In 2020 International Research Conference on Smart Computing and Systems Engineering (SCSE), pages 188–194.
Seni, G. and Elder, J. (2010). Ensemble methods in data mining: improving accuracy through combining predictions. Morgan & Claypool Publishers.
Shahpar, Z., Bardsiri, V. K., and Bardsiri, A. K. (2022). An evolutionary ensemble analogy-based software effort estimation. Software: Practice and Experience, 52(4):929–946.
Shukla, S. and Kumar, S. (2023a). Self-adaptive ensemble-based approach for software effort estimation. In 2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), pages 581–592. IEEE.
Shukla, S. and Kumar, S. (2023b). Towards ensemble-based use case point prediction. Software Quality Journal, 31(3):843–864.
Srivastava, D. K., Sharma, A. K., and Choudhary, D. (2021). 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), pages 1–5. IEEE.
Thakur, V. and Dutta, K. (2025). Machine learning based effort estimation models for software development projects related datasets with diverse features. In 2025 2nd International Conference on Computational Intelligence, Communication Technology and Networking (CICTN), pages 807–813.
Venson, E. (2020). The effects of required security on software development effort. In 2020 IEEE/ACM 42nd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), pages 166–169.
Wen, J., Li, S., Lin, Z., Hu, Y., and Huang, C. (2012). Systematic literature review of machine learning based software development effort estimation models. Information and Software Technology, 54(1):41–59.
Publicado
11/05/2026
Como Citar
SILVA, Wilamis Kleiton Nunes da; DUARTE, Rafael Batista; NASCIMENTO, Bernan Rodrigues.
Classifier Ensembles for Software Development Team Effort Estimation: A Rapid Systematic Literature Review. In: CONGRESSO IBERO-AMERICANO EM ENGENHARIA DE SOFTWARE (CIBSE), 29. , 2026, Recife/PE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 294-307.
