A Model for Non-Intrusive Capture of Metrics for Early Project Estimation in Agile Environments
Resumo
Early estimation in agile projects is essential for determining feasibility, costs, and planning. However, lacking detailed initial requirements introduces uncertainty and leads to inconsistent results. Although the literature proposes predictive models based on historical data to improve accuracy, the lack of databases tailored to each company’s context prevents practitioners from applying these models effectively. This work presents a non-intrusive model for capturing key metrics (such as software size) in agile environments to facilitate the creation of historical databases. The model will be calibrated and validated through empirical studies to support the gradual adoption of a hybrid estimation approach that combines data-driven techniques with expert judgment. Preliminary results indicate that the scarcity of historical data in practice reduces estimation accuracy, highlighting the need for automated metric capture. This approach aims to enhance the reliability of early estimations in agile projects and provide objective support for decision-making in software project management.
Palavras-chave:
early-estimation, software metrics, agile development
Referências
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Alsaadi, B. and Saeedi, K. (2022). Data-driven effort estimation techniques of agile user stories: a systematic literature review. Artificial Intelligence Review, 55:5485–5516.
Barry, B. et al. (1981). Software engineering economics. New York, 197:40.
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Cohn, M. (2005). Agile Estimating and Planning. Prentice Hall, Upper Saddle River, NJ, USA.
Commeyne, C., Abran, A., and Djouab, R. (2016). Effort estimation with story points and cosmic function points - an industry case study. In Software Measurement News.
Hacaloğlu, T. and Demirörs, O. (2018). Challenges of using software size in agile software development: A systematic literature review. Academic Papers at IWSM Mensura 2018.
Hameed, S., Elsheikh, Y., and Azzeh, M. (2023). An optimized case-based software project effort estimation using genetic algorithm. Information and Software Technology, 153:107088.
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. Natural Language for Information Systems: Communicating with Anything, Anywhere in Natural Language.
Jadhav, A., Kaur, M., and Akter, F. (2022). Evolution of software development effort and cost estimation techniques: Five decades study using automated text mining approach. Mathematical Problems in Engineering, 2022:5782587.
Kitchenham, B. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical report, Technical report, EBSE Technical Report EBSE-2007-01.
McConnell, S. (2006). Software Estimation: Demystifying the Black Art. Microsoft Press, USA.
Misra, S., Kumar, V., and Kumar, U. (2005). Goal-driven measurement framework for software innovation processes. volume 2, pages 710–716.
Prakash, B. and Viswanathan, V. (2017). A survey on software estimation techniques in traditional and agile development models. Indonesian Journal of Electrical Engineering and Computer Science, 7:867–876.
Rivera, J. G., Borrego, G., and Palacio, R. R. (2024). Early estimation in agile software development projects: A systematic mapping study. Informatics, 11.
Rosa, W. and Jardine, S. (2023). Data-driven agile software cost estimation models for dhs and dod. Journal of Systems and Software, 203:111739.
Sahab, A. and Trudel, S. (2020). Cosmic functional size automation of java web applications using the spring mvc framework. In IWSM-Mensura.
Soubra, H., Abufrikha, Y., Abran, A., et al. (2020). Towards universal cosmic size measurement automation. In Joint Proceedings of the 30th International Workshop on Software Measurementand the 15th International Conference on Software Process and ProductMeasurement (IWSM Mensura 2020), Mexico City, Mexico, October 29-30,2020.
Sudarmaningtyas, P. and Mohamed, R. B. (2021). A review article on software effort estimation in agile methodology. Pertanika Journal of Science & Technology, 29.
Tenekeci, S., Ünlü, H., Dikenelli, E., Selçuk, U., Soylu, G. K., and Demirörs, O. (2024). Predicting software size and effort from code using natural language processing. In Trudel, S., Demirörs, O., Moulla, D. K., and Hacaloglu, T., editors, Joint Proceedings of the 33rd International Workshop on Software Measurement and the 18th International Conference on Software Process and Product Measurement (IWSM-MENSURA 2024), Montréal, Canada, September 30 - October 4, 2024, volume 3852. CEUR-WS.org.
Usman, M., Mendes, E., Weidt, F., and Britto, R. (2014). Effort estimation in agile software development: A systematic literature review. In Proceedings of the 10th International Conference on Predictive Models in Software Engineering, pages 82–91. Association for Computing Machinery.
Vera, T., Ochoa, S., and Perovich, D. (2018). Survey of software development effort estimation taxonomies.
Yogi, M. K. and Chinthala, V. (2013). A hybrid approach to sizing problem in software project estimation. International Journal for Scientific Research and Development, 1(1):29–34.
Zarour, A. and Zein, S. (2019). Software development estimation techniques in industrial contexts: An exploratory multiple case-study. International Journal of Technology in Education and Science, 3:72–84.
Özgesu Özen, Özsoy, B., Aktılav, B., Güleç, E. C., and Demirörs, O. (2020). Automated estimation of functional size from code. In 2020 Turkish National Software Engineering Symposium (UYMS), pages 1–7.
Ünlü, H., Hacaloglu, T., Büber, F., Berrak, K., Leblebici, O., and Demirörs, O. (2022). Utilization of three software size measures for effort estimation in agile world: A case study. In 2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pages 239–246.
Alsaadi, B. and Saeedi, K. (2022). Data-driven effort estimation techniques of agile user stories: a systematic literature review. Artificial Intelligence Review, 55:5485–5516.
Barry, B. et al. (1981). Software engineering economics. New York, 197:40.
Beata, Przemysław, K. A. P., and Czarnacka-Chrobot (2015). Application of function points and data mining techniques for software estimation - a combined approach. In Beata, Świerczek Jaroslaw Kobyliński Andrzej, and Czarnacka-Chrobot, editors, Software Measurement, pages 96–113. Springer International Publishing.
Cohn, M. (2005). Agile Estimating and Planning. Prentice Hall, Upper Saddle River, NJ, USA.
Commeyne, C., Abran, A., and Djouab, R. (2016). Effort estimation with story points and cosmic function points - an industry case study. In Software Measurement News.
Hacaloğlu, T. and Demirörs, O. (2018). Challenges of using software size in agile software development: A systematic literature review. Academic Papers at IWSM Mensura 2018.
Hameed, S., Elsheikh, Y., and Azzeh, M. (2023). An optimized case-based software project effort estimation using genetic algorithm. Information and Software Technology, 153:107088.
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. Natural Language for Information Systems: Communicating with Anything, Anywhere in Natural Language.
Jadhav, A., Kaur, M., and Akter, F. (2022). Evolution of software development effort and cost estimation techniques: Five decades study using automated text mining approach. Mathematical Problems in Engineering, 2022:5782587.
Kitchenham, B. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical report, Technical report, EBSE Technical Report EBSE-2007-01.
McConnell, S. (2006). Software Estimation: Demystifying the Black Art. Microsoft Press, USA.
Misra, S., Kumar, V., and Kumar, U. (2005). Goal-driven measurement framework for software innovation processes. volume 2, pages 710–716.
Prakash, B. and Viswanathan, V. (2017). A survey on software estimation techniques in traditional and agile development models. Indonesian Journal of Electrical Engineering and Computer Science, 7:867–876.
Rivera, J. G., Borrego, G., and Palacio, R. R. (2024). Early estimation in agile software development projects: A systematic mapping study. Informatics, 11.
Rosa, W. and Jardine, S. (2023). Data-driven agile software cost estimation models for dhs and dod. Journal of Systems and Software, 203:111739.
Sahab, A. and Trudel, S. (2020). Cosmic functional size automation of java web applications using the spring mvc framework. In IWSM-Mensura.
Soubra, H., Abufrikha, Y., Abran, A., et al. (2020). Towards universal cosmic size measurement automation. In Joint Proceedings of the 30th International Workshop on Software Measurementand the 15th International Conference on Software Process and ProductMeasurement (IWSM Mensura 2020), Mexico City, Mexico, October 29-30,2020.
Sudarmaningtyas, P. and Mohamed, R. B. (2021). A review article on software effort estimation in agile methodology. Pertanika Journal of Science & Technology, 29.
Tenekeci, S., Ünlü, H., Dikenelli, E., Selçuk, U., Soylu, G. K., and Demirörs, O. (2024). Predicting software size and effort from code using natural language processing. In Trudel, S., Demirörs, O., Moulla, D. K., and Hacaloglu, T., editors, Joint Proceedings of the 33rd International Workshop on Software Measurement and the 18th International Conference on Software Process and Product Measurement (IWSM-MENSURA 2024), Montréal, Canada, September 30 - October 4, 2024, volume 3852. CEUR-WS.org.
Usman, M., Mendes, E., Weidt, F., and Britto, R. (2014). Effort estimation in agile software development: A systematic literature review. In Proceedings of the 10th International Conference on Predictive Models in Software Engineering, pages 82–91. Association for Computing Machinery.
Vera, T., Ochoa, S., and Perovich, D. (2018). Survey of software development effort estimation taxonomies.
Yogi, M. K. and Chinthala, V. (2013). A hybrid approach to sizing problem in software project estimation. International Journal for Scientific Research and Development, 1(1):29–34.
Zarour, A. and Zein, S. (2019). Software development estimation techniques in industrial contexts: An exploratory multiple case-study. International Journal of Technology in Education and Science, 3:72–84.
Özgesu Özen, Özsoy, B., Aktılav, B., Güleç, E. C., and Demirörs, O. (2020). Automated estimation of functional size from code. In 2020 Turkish National Software Engineering Symposium (UYMS), pages 1–7.
Ünlü, H., Hacaloglu, T., Büber, F., Berrak, K., Leblebici, O., and Demirörs, O. (2022). Utilization of three software size measures for effort estimation in agile world: A case study. In 2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pages 239–246.
Publicado
12/05/2025
Como Citar
IBARRA, José Gamaliel Rivera.
A Model for Non-Intrusive Capture of Metrics for Early Project Estimation in Agile Environments. In: CONGRESSO IBERO-AMERICANO EM ENGENHARIA DE SOFTWARE (CIBSE), 28. , 2025, Ciudad Real/Espanha.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 304-311.
DOI: https://doi.org/10.5753/cibse.2025.35317.
