User Story Estimation using Natural Language Processing and Deep Learning: A Comparative Study

Daniel de Oliveira Silva; Alinne Cristinne Corrêa Souza; Francisco Carlos Monteiro Souza; Silvio Ricardo Rodrigues Sanches

doi:10.5753/jbcs.2026.5860

Authors

Daniel de Oliveira Silva Federal University of Technology – Paraná - Cornélio Procópio https://orcid.org/0009-0000-9850-7181
Alinne Cristinne Corrêa Souza Federal University of Technology – Paraná - Dois Vizinhos https://orcid.org/0000-0002-2525-7801
Francisco Carlos Monteiro Souza Federal University of Technology – Paraná - Dois Vizinhos https://orcid.org/0000-0002-8953-8653
Silvio Ricardo Rodrigues Sanches Federal University of Technology – Paraná - Cornélio Procópio https://orcid.org/0000-0003-3635-7477

DOI:

https://doi.org/10.5753/jbcs.2026.5860

Keywords:

user story, effort estimation, natural language processing, deep learning

Abstract

Effort estimation is a fundamental activity in software development, guiding task prioritization, resource allocation, and cost planning. Traditional techniques, such as Planning Poker, rely heavily on team subjectivity and experience, which can compromise their effectiveness in certain contexts. This study explores how machine learning and natural language processing techniques can increase the accuracy of effort estimation for user stories. To understand the current state of research in this area, a systematic mapping was conducted to identify the main databases, techniques, and methods used to estimate effort based on textual requirements. Guided by the mapping, a comparative experimental study was performed using the FastText and XLNet language models, combined with a deep neural network, on a dataset of 23,313 user stories. The results indicate that XLNet outperformed FastText in most evaluation metrics, achieving a Mean Absolute Error (MAE) of 3.77, a Mean Squared Error (MSE) of 79.94, and a Median Absolute Error (MdAE) of 1.93. Furthermore, the proposed approach performed competitively compared to related works. These findings demonstrate the potential of deep learning models to assist developers by providing more accurate, consistent, and objective estimates for user story effort.

Downloads

Download data is not yet available.

References

Abadeer, M. and Sabetzadeh, M. (2021). Machine learning-based estimation of story points in agile development: Industrial experience and lessons learned. In 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW), pages 106-115. IEEE. DOI: 10.1109/REW53955.2021.00022.

Abdukalykov, R. (2011). A New Methodology for Quantifying the Impact of Non-Functional Requirements on Software Effort Estimation. PhD thesis, Concordia University. Available at:[link].

Abnane, I., Hosni, M., Idri, A., and Abran, A. (2019). Analogy software effort estimation using ensemble knn imputation. In 2019 45th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pages 228-235. IEEE. DOI: 10.1109/SEAA.2019.00044.

Abrahamsson, P., Fronza, I., Moser, R., Vlasenko, J., and Pedrycz, W. (2011). Predicting development effort from user stories. In 2011 International Symposium on Empirical Software Engineering and Measurement, pages 400-403. IEEE. DOI: 10.1109/ESEM.2011.58.

AG, P. V., K, A. K., and Varadarajan, V. (2021). Estimating software development efforts using a random forest-based stacked ensemble approach. Electronics, 10(10):1195. DOI: 10.3390/electronics10101195.

Aranda, J. and Easterbrook, S. (2005). Anchoring and adjustment in software estimation. In Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering, pages 346-355. DOI: 10.1145/1095430.1081761.

Araujo, R. d. A., Oliveira, A. L., and Meira, S. (2017). A class of hybrid multilayer perceptrons for software development effort estimation problems. Expert Systems with Applications, 90:1-12. DOI: 10.1016/j.eswa.2017.07.050.

Arora, M., Sharma, A., Katoch, S., Malviya, M., and Chopra, S. (2021). A state of the art regressor model’s comparison for effort estimation of agile software. In 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), pages 211-215. IEEE. DOI: 10.1109/ICIEM51511.2021.9445345.

Azzeh, M., Nassif, A. B., and Banitaan, S. (2018). Comparative analysis of soft computing techniques for predicting software effort based use case points. Iet Software, 12(1):19-29. DOI: 10.1049/iet-sen.2016.0322.

Azzeh, M., Nassif, A. B., and Minku, L. L. (2015). An empirical evaluation of ensemble adjustment methods for analogy-based effort estimation. Journal of Systems and Software, 103:36-52. DOI: 10.1016/j.jss.2015.01.028.

Baratto, G. J. (2022). Comparação entre os modelos pré-treinados gpt-3 e bert na estimativa de esforço de software por analogia a partir de requisitos textuais. B.S. thesis, Universidade Tecnológica Federal do Paraná. Available at:[link].

Basili, V. R. and Weiss, D. M. (1986). A methodology for collecting valid software engineering data. IEEE Transactions on Software Engineering, 10(6):728-738. DOI: 10.1109/TSE.1984.5010301.

Bengio, Y., Courville, A., and Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, 35(8):1798-1828. DOI: 10.1109/tpami.2013.50.

Bhat, J. M., Gupta, M., and Murthy, S. N. (2006). Overcoming requirements engineering challenges: Lessons from offshore outsourcing. IEEE software, 23(5):38-44. DOI: 10.1109/ms.2006.137.

Bilgaiyan, S., Mishra, S., and Das, M. (2019). Effort estimation in agile software development using experimental validation of neural network models. International Journal of Information Technology, 11(3):569-573. DOI: 10.1007/s41870-018-0131-2.

Boehm, B., Abts, C., and Chulani, S. (2000). Software development cost estimation approaches—a survey. Annals of software engineering, 10(1-4):177-205. DOI: 10.1023/A:1018991717352.

Bojanowski, P., Grave, E., Joulin, A., and Mikolov, T. (2017). Enriching word vectors with subword information. Transactions of the association for computational linguistics, 5:135-146. DOI: 10.1023/A:1018991717352.

Braga, P. L., Oliveira, A. L., and Meira, S. R. (2007). Software effort estimation using machine learning techniques with robust confidence intervals. In 7th international conference on hybrid intelligent systems (HIS 2007), pages 352-357. IEEE. DOI: 10.1109/HIS.2007.56.

Britto, R., Freitas, V., Mendes, E., and Usman, M. (2014). Effort estimation in global software development: A systematic literature review. In 2014 IEEE 9th International Conference on Global Software Engineering, pages 135-144. IEEE. DOI: 10.1109/ICGSE.2014.11.

Cambria, E. and White, B. (2014). Jumping nlp curves: A review of natural language processing research. IEEE Computational intelligence magazine, 9(2):48-57. DOI: 10.1109/MCI.2014.2307227.

Choetkiertikul, M., Dam, H. K., Tran, T., Pham, T., Ghose, A., and Menzies, T. (2018). A deep learning model for estimating story points. IEEE Transactions on Software Engineering, 45(7):637-656. DOI: 10.1109/TSE.2018.2792473.

Choi, S., Park, S., and Sugumaran, V. (2012). A rule-based approach for estimating software development cost using function point and goal and scenario based requirements. Expert Systems with Applications, 39(1):406-418. DOI: 10.1016/j.eswa.2011.07.029.

Cohn, M. (2005). Agile estimating and planning. Pearson Education. Book.

Costa, L. A. and Salvador, L. d. N. (2015). Ambiente de aprendizagem presencial e virtual integrados com a computação ubíqua: Um mapeamento sistemático da literatura. In Memorias del XX Congresso Internacional de Informática Educativa, TISE, volume 11, pages 211-220. Available at:[link].

de Morais, R. A. (2021). Deep learning based models for software effort estimation using story points in agile environments. DOI: 10.7939/r3-jcf5-8x08.

Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. DOI: 10.18653/v1/N19-1423.

Dhir, S., Kumar, D., and Singh, V. (2017). An estimation technique in agile archetype using story points and function point analysis. International Journal of Process Management and Benchmarking, 7(4):518-539. DOI: 10.1504/IJPMB.2017.086933.

Dragicevic, S., Celar, S., and Turic, M. (2017). Bayesian network model for task effort estimation in agile software development. Journal of systems and software, 127:109-119. DOI: 10.1016/j.jss.2017.01.027.

Fávero, E. M. D. B., Casanova, D., and Pimentel, A. R. (2022). Se3m: A model for software effort estimation using pre-trained embedding models. Information and Software Technology, 147:106886. DOI: 10.1016/j.infsof.2022.106886.

Fávero, E. M. D. B., Pereira, R., Pimentel, A. R., and Casanova, D. (2018). Analogy-based effort estimation: A systematic mapping of literature. INFOCOMP Journal of Computer Science, 17(2):07-22. Available at:[link].

Fedotova, O., Teixeira, L., Alvelos, H., et al. (2013). Software effort estimation with multiple linear regression: Review and practical application. J. Inf. Sci. Eng., 29(5):925-945. DOI: 10.6688/JISE.2013.29.5.8.

Fu, M. and Tantithamthavorn, C. (2022). Gpt2sp: A transformer-based agile story point estimation approach. IEEE Transactions on Software Engineering, 49(2):611-625. DOI: 10.1109/TSE.2022.3158252.

Ghosal, S. and Jain, A. (2023). Depression and suicide risk detection on social media using fasttext embedding and xgboost classifier. Procedia Computer Science, 218:1631-1639. DOI: 10.1016/j.procs.2023.01.141.

Goldberg, Y. (2022). Neural network methods for natural language processing. Springer Nature. DOI: 10.1007/978-3-031-02165-7.

Grenning, J. (2002). Planning poker or how to avoid analysis paralysis while release planning. Hawthorn Woods: Renaissance Software Consulting, 3:22-23. Available at:[link].

Gultekin, M. and Kalipsiz, O. (2020). Story point-based effort estimation model with machine learning techniques. International Journal of Software Engineering and Knowledge Engineering, 30(01):43-66. DOI: 10.1142/S0218194020500035.

Gupta, N. and Mahapatra, R. P. (2022). Automated software effort estimation for agile development system by heuristically improved hybrid learning. Concurrency and Computation: Practice and Experience, 34(25):e7267. DOI: 10.1002/cpe.7267.

Haj-Yahia, Z., Sieg, A., and Deleris, L. A. (2019). Towards unsupervised text classification leveraging experts and word embeddings. In Proceedings of the 57th annual meeting of the Association for Computational Linguistics, pages 371-379. DOI: 10.18653/v1/p19-1036.

Hamouda, A. E. D. (2014). Using agile story points as an estimation technique in cmmi organizations. In 2014 agile conference, pages 16-23. IEEE. DOI: 10.1109/AGILE.2014.11.

Hemrajani, M. V. N. and N, V. (2021). Predicting effort of agile software projects using linear regression, ridge regression and logistic regression. Available at:[link].

Hidmi, O. and Sakar, B. E. (2017). Software development effort estimation using ensemble machine learning. Int. J. Comput. Commun. Instrum. Eng, 4(1):143-147. DOI: 10.15242/IJCCIE.E0317026.

Hirschberg, J. and Manning, C. D. (2015). Advances in natural language processing. Science, 349(6245):261-266. DOI: 10.1126/science.aaa8685.

Hussain, I., Kosseim, L., and Ormandjieva, O. (2013). Approximation of cosmic functional size to support early effort estimation in agile. Data & Knowledge Engineering, 85:2-14. DOI: 10.1016/j.datak.2012.06.005.

Hussein, L. A., Nassar, K. A., and Naser, M. (2017). Recurrent neural network based prediction of software effort. International Journal of Computer Applications, 975:8887. DOI: 10.5120/ijca2017915664.

Jobera Editorial Team (2025). Software project failure statistics: Insights and trends. Available at:[link]. Accessed: 2025-04-19.

Jurafsky, D. and Martin, J. H. (2019). Speech and language processing (3rd (draft) ed.). Available at:[link].

Kanmani, S., Kathiravan, J., Kumar, S. S., and Shanmugam, M. (2007). Neural network based effort estimation using class points for oo systems. In 2007 International Conference on Computing: Theory and Applications (ICCTA'07), pages 261-266. IEEE. DOI: 10.1109/ICCTA.2007.89.

Kassem, H., Mahar, K., and Saad, A. A. (2023). Story point estimation using issue reports with deep attention neural network. e-Informatica Software Engineering Journal, 17(1). DOI: 10.37190/e-Inf230104.

Kaushik, A. and Singal, N. (2022). A hybrid model of wavelet neural network and metaheuristic algorithm for software development effort estimation. International Journal of Information Technology, 14(3):1689-1698. DOI: 10.1007/s41870-019-00339-1.

Kaushik, A., Tayal, D. K., and Yadav, K. (2020). A comparative analysis on effort estimation for agile and non-agile software projects using dbn-alo. Arabian Journal for Science and Engineering, 45(4):2605-2618. DOI: 10.1007/s13369-019-04250-6.

Keele, S. et al. (2007). Guidelines for performing systematic literature reviews in software engineering. DOI: 10.1109/LA-CCI.2017.8285683.

Khan, M. W. and Qureshi, I. (2014). Neural network based software effort estimation: a survey. International Journal of Advanced Networking and Applications, 5(4):1990. Available at:[link].

Khuat, T. T. and Le, M. H. (2016). An effort estimation approach for agile software development using fireworks algorithm optimized neural network. Int. J. Comput. Sci. Inf. Secur.(IJCSIS), 14:122-130. Available at:[link].

Khuat, T. T. and Le, M. H. (2018). A novel hybrid abc-pso algorithm for effort estimation of software projects using agile methodologies. Journal of Intelligent Systems, 27(3):489-506. DOI: 10.1515/jisys-2016-0294.

Kitchenham, B. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical Report EBSE-2007-01, Keele University and Durham University. Available at:[link].

Kocaguneli, E., Menzies, T., and Keung, J. W. (2011). On the value of ensemble effort estimation. IEEE Transactions on Software Engineering, 38(6):1403-1416. DOI: 10.1109/TSE.2011.111.

Kultur, Y., Turhan, B., and Bener, A. B. (2008). Enna: software effort estimation using ensemble of neural networks with associative memory. In Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering, pages 330-338. DOI: 10.1145/1453101.1453148.

Kumar, P. S., Behera, H. S., Kumari, A., Nayak, J., and Naik, B. (2020). Advancement from neural networks to deep learning in software effort estimation: Perspective of two decades. Computer Science Review, 38:100288. DOI: 10.1016/j.cosrev.2020.100288.

Kumar, S., Arora, M., Chopra, S., et al. (2022). A review of effort estimation in agile software development using machine learning techniques. In 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA), pages 416-422. IEEE. DOI: 10.1109/icirca54612.2022.9985542.

Malgonde, O. and Chari, K. (2019). An ensemble-based model for predicting agile software development effort. Empirical Software Engineering, 24:1017-1055. DOI: 10.1007/s10664-018-9647-0.

Marapelli, B., Carie, A., and Islam, S. M. (2020). Rnn-cnn model: A bi-directional long short-term memory deep learning network for story point estimation. In 2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA), pages 1-7. IEEE. DOI: 10.1109/CITISIA50690.2020.9371770.

Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013a). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781. DOI: 10.48550/arxiv.1301.3781.

Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. (2013b). Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems, 26. DOI: 10.48550/arxiv.1310.4546.

Minku, L. L. and Yao, X. (2013). Ensembles and locality: Insight on improving software effort estimation. Information and Software Technology, 55(8):1512-1528. DOI: 10.1016/j.infsof.2012.09.012.

Moharreri, K., Sapre, A. V., Ramanathan, J., and Ramnath, R. (2016). Cost-effective supervised learning models for software effort estimation in agile environments. In 2016 IEEE 40th Annual computer software and applications conference (COMPSAC), volume 2, pages 135-140. IEEE. DOI: 10.1109/COMPSAC.2016.85.

Najm, A., Zakrani, A., and Marzak, A. (2019). Decision trees based software development effort estimation: A systematic mapping study. In 2019 International Conference of Computer Science and Renewable Energies (ICCSRE), pages 1-6. IEEE. DOI: 10.1109/iccsre.2019.8807544.

Naseem, U., Razzak, I., Khan, S. K., and Prasad, M. (2021). A comprehensive survey on word representation models: From classical to state-of-the-art word representation language models. Transactions on Asian and Low-Resource Language Information Processing, 20(5):1-35. DOI: 10.1145/3434237.

Nassif, A. B., Azzeh, M., Capretz, L. F., and Ho, D. (2013). A comparison between decision trees and decision tree forest models for software development effort estimation. In 2013 Third International Conference on Communications and Information Technology (ICCIT), pages 220-224. IEEE. DOI: 10.1109/ICCITechnology.2013.6579553.

Panda, A., Satapathy, S. M., and Rath, S. K. (2015). Empirical validation of neural network models for agile software effort estimation based on story points. Procedia Computer Science, 57:772-781. DOI: 10.1016/j.procs.2015.07.474.

Pennington, J., Socher, R., and Manning, C. D. (2014). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532-1543. DOI: 10.3115/v1/d14-1162.

Petersen, K., Feldt, R., Mujtaba, S., and Mattsson, M. (2008). Systematic mapping studies in software engineering. In Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering, pages 68-77. BCS Learning & Development. DOI: 10.14236/ewic/EASE2008.8.

Petersen, K., Vakkalanka, S., and Kuzniarz, L. (2015). Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology, 64:1-18. DOI: 10.1016/j.infsof.2015.03.007.

Phan, H. and Jannesari, A. (2022a). Heterogeneous graph neural networks for software effort estimation. In Proceedings of the 16th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, pages 103-113. DOI: 10.1145/3544902.3546248.

Phan, H. and Jannesari, A. (2022b). Story point level classification by text level graph neural network. In Proceedings of the 1st International Workshop on Natural Language-based Software Engineering, pages 75-78. DOI: 10.1145/3528588.3528654.

Porru, S., Murgia, A., Demeyer, S., Marchesi, M., and Tonelli, R. (2016). Estimating story points from issue reports. In Proceedings of the the 12th international conference on predictive models and data analytics in software engineering, pages 1-10. DOI: 10.1145/2972958.2972959.

Pospieszny, P., Czarnacka-Chrobot, B., and Kobylinski, A. (2018). An effective approach for software project effort and duration estimation with machine learning algorithms. Journal of Systems and Software, 137:184-196. DOI: 10.1016/j.jss.2017.11.066.

Prasada Rao, C., Siva Kumar, P., Rama Sree, S., and Devi, J. (2018). An agile effort estimation based on story points using machine learning techniques. In Proceedings of the Second International Conference on Computational Intelligence and Informatics: ICCII 2017, pages 209-219. Springer. DOI: 10.1007/978-981-10-8228-3_20.

Praynlin, E. and Latha, P. (2013). Performance analysis of software effort estimation models using neural networks. International Journal of Information Technology and Computer Science (IJITCS), 5(9):101-107. DOI: 10.5815/ijitcs.2013.09.11.

Pressman, R. S. (2011). Engenharia de software: uma abordagem profissional. 7ª edição. Ed: McGraw Hill. Book.

Ramchurreetoo, Y. and Hurbungs, V. (2022). A multiclass classification model to estimate agile user stories. In 2022 3rd International Conference on Next Generation Computing Applications (NextComp), pages 1-5. IEEE. DOI: 10.1109/NextComp55567.2022.9932190.

Ramessur, M. A. and Nagowah, S. D. (2021). A predictive model to estimate effort in a sprint using machine learning techniques. International Journal of Information Technology, 13(3):1101-1110. DOI: 10.1007/s41870-021-00669-z.

Rodríguez Sánchez, E., Vázquez Santacruz, E. F., and Cervantes Maceda, H. (2023). Effort and cost estimation using decision tree techniques and story points in agile software development. Mathematics, 11(6):1477. DOI: 10.3390/math11061477.

Russell, S. (2016). Artificial Intelligence: A Modern Approach, eBook, Global Edition. Pearson Education, Limited. Book.

Sánchez, E. R., Maceda, H. C., and Santacruz, E. V. (2022). Software effort estimation for agile software development using a strategy based on k-nearest neighbors algorithm. In 2022 IEEE Mexican International Conference on Computer Science (ENC), pages 1-6. IEEE. DOI: 10.1109/ENC56672.2022.9882947.

Santos, I., Nedjah, N., and de Macedo Mourelle, L. (2017). Sentiment analysis using convolutional neural network with fasttext embeddings. In 2017 IEEE Latin American conference on computational intelligence (LA-CCI), pages 1-5. IEEE. DOI: 10.1109/LA-CCI.2017.8285683.

Sarro, F., Moussa, R., Petrozziello, A., and Harman, M. (2020). Learning from mistakes: Machine learning enhanced human expert effort estimates. IEEE Transactions on Software Engineering, 48(6):1868-1882. DOI: 10.1109/TSE.2020.3040793.

Satapathy, S. M. and Rath, S. K. (2017). Empirical assessment of machine learning models for agile software development effort estimation using story points. Innovations in Systems and Software Engineering, 13(2-3):191-200. DOI: 10.1007/s11334-017-0288-z.

Scott, E. and Pfahl, D. (2018). Using developers' features to estimate story points. In Proceedings of the 2018 International Conference on Software and System Process, pages 106-110. DOI: 10.1145/3202710.3203160.

Sharma, A. and Chaudhary, N. (2020). Linear regression model for agile software development effort estimation. In 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE), pages 1-4. IEEE. DOI: 10.1109/ICRAIE51050.2020.9358309.

Shepperd, M. (2007). Software project economics: a roadmap. In Future of Software Engineering (FOSE'07), pages 304-315. IEEE. DOI: 10.1109/FOSE.2007.23.

Sinclair, J. and Wynne, M. (2004). Developing linguistic corpora: A guide to good practice. Ahds literature, languages and linguistics. Available at:[link].

Singh, A. and Kumar, M. (2020). Comparative analysis on prediction of software effort estimation using machine learning techniques. In Proceedings of the International Conference on Innovative Computing & Communications (ICICC). DOI: 10.2139/ssrn.3565813.

Soares, R. G. (2018). Effort estimation via text classification and autoencoders. In 2018 International Joint Conference on Neural Networks (IJCNN), pages 01-08. IEEE. DOI: 10.1109/IJCNN.2018.8489030.

SOMMERVILLE, I. (2004). Engenharia de Software. São Paulo: Pearson Addison-Wesley. Book.

Sommerville, I. (2011). Engenharia de software-8ª edição 2007. Ed Person Education. Book.

Sree, S., Rao, P., and Mounika, M. (2017). An early-stage software effort estimation in agile methodology based on user stories using machine learning techniques. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 5. Available at:[link].

Srinivasan, K. and Fisher, D. (1995). Machine learning approaches to estimating software development effort. IEEE Transactions on Software Engineering, 21(2):126-137. DOI: 10.1109/32.345828.

Sudarmaningtyas, P. and Mohamed, R. (2021). A review article on software effort estimation in agile methodology. Pertanika Journal of Science & Technology, 29(2):837-861. DOI: 10.47836/pjst.29.2.08.

Suresh, Y. (2022). Effective ann model based on neuro-evolution mechanism for realistic software estimates in the early phase of software development. International Journal of Advanced Computer Science and Applications, 13(2). DOI: 10.14569/IJACSA.2022.0130223.

Suresh Kumar, P., Behera, H., Nayak, J., and Naik, B. (2022). A pragmatic ensemble learning approach for effective software effort estimation. Innovations in Systems and Software Engineering, 18(2):283-299. DOI: 10.1007/s11334-020-00379-y.

Tawosi, V., Moussa, R., and Sarro, F. (2022). Agile effort estimation: Have we solved the problem yet? insights from a replication study. IEEE Transactions on Software Engineering, 49(4):2677-2697. DOI: 10.1109/TSE.2022.3228739.

Turic, M., Celar, S., Dragicevic, S., and Vickovic, L. (2023). Advanced bayesian network for task effort estimation in agile software development. Applied Sciences, 13(16):9465. DOI: 10.3390/app13169465.

Van Houdt, G., Mosquera, C., and Nápoles, G. (2020). A review on the long short-term memory model. Artificial Intelligence Review, 53:5929-5955. DOI: 10.1007/s10462-020-09838-1.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30. DOI: 10.65215/nxvz2v36.

Wang, C. and Zhang, F. (2022). The performance of improved xlnet on text classification. In Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), volume 12329, pages 154-159. SPIE. DOI: 10.1117/12.2646785.

Wazlawick, R. (2010). Análise e Projeto de Sistemas de Informação Orientados, volume 2. Elsevier Brasil. Book.

Wohlin, C. (2014). Guidelines for snowballing in systematic literature studies and a replication in software engineering. In Proceedings of the 18th international conference on evaluation and assessment in software engineering, pages 1-10. DOI: 10.1145/2601248.2601268.

Wohlin, C., Runeson, P., Host, M., Ohlsson, M. C., Regnell, B., and Wesslen, A. (2012). Experimentation in Software Engineering: An Introduction. Springer-Verlag Berlin Heidelberg, 1st. edition. DOI: 10.1007/978-3-662-69306-3.

Yang, Z. (2019). Xlnet: Generalized autoregressive pretraining for language understanding. arXiv preprint arXiv:1906.08237. DOI: 10.1145/3491065.

Yousef, Q. M., Alshaer, Y. A., and Alhammad, N. K. (2017). Dragonfly estimator: a hybrid software projects’ efforts estimation model using artificial neural network and dragonfly algorithm. Int. J. Comput. Sci. Netw. Secur, 17(9):108-120. Available at:[link].

Zakrani, A., Najm, A., and Marzak, A. (2018). Support vector regression based on grid-search method for agile software effort prediction. In 2018 IEEE 5th International Congress on Information Science and Technology (CiSt), pages 1-6. IEEE. DOI: 10.1109/CIST.2018.8596370.

Zhang, D., Xu, H., Su, Z., and Xu, Y. (2015). Chinese comments sentiment classification based on word2vec and svmperf. Expert Systems with Applications, 42(4):1857-1863. DOI: 10.1016/j.eswa.2014.09.011.