Estimativa de Esforço em Atividades de Manutenção de Software: Um Mapeamento Sistemático
Resumo
Durante o ciclo de vida do software, o correto levantamento de estimativas de esforço permite que a equipe tome decisões sobre como será o andamento das atividades de desenvolvimento e manutenção, qual a viabilidade de novas alterações, e se estas serão entregues de acordo com os prazos. Neste artigo, é apresentado um mapeamento sistemático com o objetivo de identificar evidências na literatura com relação a métricas e abordagens para o cálculo de estimativa de esforço durante a fase de manutenção de software. Foram analisados 521 estudos e selecionados 17 estudos primários; a maioria das abordagens utiliza de métricas ou dados históricos para estimativa de novas atividades, e apenas um framework foi proposto, o que ressalta a importância de gerar novas ferramentas que facilitem a geração das novas estimativas.
Palavras-chave:
Estimativa de esforço, Manutenção de Software
Referências
Ahn, Y., Suh, J., Kim, S., and Kim, H. (2003). The software maintenance project effort estimation model-based on function points. Software maintenance and evolution: Research and practice, 15(2):71–85.
Alomari, H. W., Collard, M. L., and Maletic, J. I. (2014). A slice-based estimation approach for maintenance effort. In 30th International Conference on Software Maintenance and Evolution, pages 81–90.
Bharathi, V. and Shastry, U. (2011). Neural network-based effort prediction model for maintena
Chandra, D., Choudhary, M., and Gupta, D. (2017). Prophecy of software maintenance effort with univariate and multivariate approach: By using support vector machine learning technique with radial basis kernel function. In 6th International Conference on Computing, Communication and Automation, pages 876–880.
De Lucia, A., Pompella, E., and Stefanucci, S. (2005). Assessing effort estimation models for corrective maintenance through empirical studies. Information and Software Technology, 47(1):3–15.
Erlikh, L. (2000). Leveraging legacy system dollars for e-business.IT Professional, 2(3):17–23.
Hayes, J. H., Patel, S. C., and Zhao, L. (2004). A metrics-based software maintenance effort model. In8thEuropean Conference on Software Maintenance and Reengineering, pages 254–258.
Hira, A. and Boehm, B. (2018). COSMIC function points evaluation for software maintenance. In11thInnovations in Software Engineering Conference, page 4.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University,33(2004):1–26.
Lélis, C. A., Miguel, M. A., Araújo, M. A. P., David, J. M. N., and Braga, R. (2018). AD-reputation: a reputation-based approach to support effort estimation. In Information Technology – New Generations, pages 621–626. Springer.
Leung, H. K. (2002). Estimating maintenance effort by analogy. Empirical Software Engineering, 7(2):157–175.
Lientz, B. and Swanson, E. (1980).Software maintenance management: a study of the maintenance of computer application software in 487 data processing organizations. Addison-Wesley.
Miguel, M. A., Araújo, M. A. P., David, J. M. N., and Braga, R. (2016). A framework to support effort estimation on software maintenance and evolution activities. In 12th Brazilian Symposium on Information Systems: Information Systems in the Cloud Computing Era, pages 232–239.
Nguyen, V., Boehm, B., and Danphitsanuphan, P. (2011). A controlled experiment in assessing and estimating software maintenance tasks. Information and Software Technology, 53(6):682–691.
Niessink, F. and Van Vliet, H. (1997). Predicting maintenance effort with function points. In 13th International Conference on Software Maintenance, pages 32–39.
Pillai, S. and Madhukumar, R. (2019). An experiment to improve expert judgment software estimation through work breakdown structure. Innovative Technology and Exploring Engineering, 8(7):2278–3075.
Shukla, R. and Misra, A. K. (2008). Estimating software maintenance effort: a neural network approach. In 1st India Software Engineering Conference, pages 107–112.
Song, T.-H., Yoon, K. -A., and Bae, D.-H. (2007). An approach to probabilistic effort estimation for military avionics software maintenance by considering structural characteristics. In 14th Asia-Pacific Software Engineering Conference, pages 406–413.
Tenório Jr, N. N., Ribeiro, M. B., and Ruiz, D. D. (2008). A quasi-experiment for effort and defect estimation using least square linear regression and function points. In 32nd Annual IEEE Software Engineering Workshop, pages 143–151.
Thaw, T., Aung, M. P., Wah, N. L., Nyein, S. S., Phyo, Z. L., and Htun, K. Z. (2010). Comparison for the accuracy of defect fix effort estimation. In 2nd International Conference on Computer Engineering and Technology, pages 550–554.
Vazquez, C. E., Simões, G. S., and Albert, R. M. (2013).Análise de Pontos de Função: Medição, estimativas e gerenciamento de projetos de software. Érica, São Paulo.
Alomari, H. W., Collard, M. L., and Maletic, J. I. (2014). A slice-based estimation approach for maintenance effort. In 30th International Conference on Software Maintenance and Evolution, pages 81–90.
Bharathi, V. and Shastry, U. (2011). Neural network-based effort prediction model for maintena
Chandra, D., Choudhary, M., and Gupta, D. (2017). Prophecy of software maintenance effort with univariate and multivariate approach: By using support vector machine learning technique with radial basis kernel function. In 6th International Conference on Computing, Communication and Automation, pages 876–880.
De Lucia, A., Pompella, E., and Stefanucci, S. (2005). Assessing effort estimation models for corrective maintenance through empirical studies. Information and Software Technology, 47(1):3–15.
Erlikh, L. (2000). Leveraging legacy system dollars for e-business.IT Professional, 2(3):17–23.
Hayes, J. H., Patel, S. C., and Zhao, L. (2004). A metrics-based software maintenance effort model. In8thEuropean Conference on Software Maintenance and Reengineering, pages 254–258.
Hira, A. and Boehm, B. (2018). COSMIC function points evaluation for software maintenance. In11thInnovations in Software Engineering Conference, page 4.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University,33(2004):1–26.
Lélis, C. A., Miguel, M. A., Araújo, M. A. P., David, J. M. N., and Braga, R. (2018). AD-reputation: a reputation-based approach to support effort estimation. In Information Technology – New Generations, pages 621–626. Springer.
Leung, H. K. (2002). Estimating maintenance effort by analogy. Empirical Software Engineering, 7(2):157–175.
Lientz, B. and Swanson, E. (1980).Software maintenance management: a study of the maintenance of computer application software in 487 data processing organizations. Addison-Wesley.
Miguel, M. A., Araújo, M. A. P., David, J. M. N., and Braga, R. (2016). A framework to support effort estimation on software maintenance and evolution activities. In 12th Brazilian Symposium on Information Systems: Information Systems in the Cloud Computing Era, pages 232–239.
Nguyen, V., Boehm, B., and Danphitsanuphan, P. (2011). A controlled experiment in assessing and estimating software maintenance tasks. Information and Software Technology, 53(6):682–691.
Niessink, F. and Van Vliet, H. (1997). Predicting maintenance effort with function points. In 13th International Conference on Software Maintenance, pages 32–39.
Pillai, S. and Madhukumar, R. (2019). An experiment to improve expert judgment software estimation through work breakdown structure. Innovative Technology and Exploring Engineering, 8(7):2278–3075.
Shukla, R. and Misra, A. K. (2008). Estimating software maintenance effort: a neural network approach. In 1st India Software Engineering Conference, pages 107–112.
Song, T.-H., Yoon, K. -A., and Bae, D.-H. (2007). An approach to probabilistic effort estimation for military avionics software maintenance by considering structural characteristics. In 14th Asia-Pacific Software Engineering Conference, pages 406–413.
Tenório Jr, N. N., Ribeiro, M. B., and Ruiz, D. D. (2008). A quasi-experiment for effort and defect estimation using least square linear regression and function points. In 32nd Annual IEEE Software Engineering Workshop, pages 143–151.
Thaw, T., Aung, M. P., Wah, N. L., Nyein, S. S., Phyo, Z. L., and Htun, K. Z. (2010). Comparison for the accuracy of defect fix effort estimation. In 2nd International Conference on Computer Engineering and Technology, pages 550–554.
Vazquez, C. E., Simões, G. S., and Albert, R. M. (2013).Análise de Pontos de Função: Medição, estimativas e gerenciamento de projetos de software. Érica, São Paulo.
Publicado
11/11/2020
Como Citar
VIESSELI, Kaliane; SILVA, Arielyn; SANTOS, Gustavo.
Estimativa de Esforço em Atividades de Manutenção de Software: Um Mapeamento Sistemático. In: ESCOLA REGIONAL DE ENGENHARIA DE SOFTWARE (ERES), 4. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 97-105.
DOI: https://doi.org/10.5753/eres.2020.13720.
