Estimativa de Esforço em Projetos Ágeis de Software Utilizando Mapas de Kohonen
Resumo
O desenvolvimento de software por meio de metodos ágeis está diretamente ligado a execução das várias atividades planejadas ao longo das iterações. Boas estimativas de prazos tornam projeto estável, a equipe de desenvolvimento mais segura e o cliente mais satisfeito. No entanto, estimar o prazo de desenvolvimento de uma tarefa é algo sensível a erros, uma vez que todas as tecnicas possuem alguma limitação prática de uso. Este trabalho tem como objetivo a realização de um estudo sobre o uso de mapas auto-organizáveis de Kohonen para auxiliar nessas estimativas, fornecendo uma estimativa inicial do tempo de duração de uma tarefa, utilizando como base medidas obtidas de tarefas semelhantes e previamente realizadas. Para avaliar aabordagem foram utilizados dados obtidos de uma empresa de desenvolvimento de software local. A partir da avalição foi possível tirar conclusões relativas a qualidade dos registros feitos pelos desenvolvedores dessa empresa, e avaliar a precisao da estimativa feita pela abordagem.
Palavras-chave:
Estimativa, Projetos Ageis, Mapas de Kohonen
Referências
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Attarzadeh, I. and Ow, S. H. (2011). Improving estimation accuracy of the cocomo ii using an adaptive fuzzy logic model. In 2011 IEEE International Conference on Fuzzy Systems (FUZZ), pages 2458–2464.
Beck, K., Beedle, M., van Bennekum, A., Cockburn, A., Cunningham, W., Fowler, M., Grenning, J., Highsmith, J., Hunt, A., Jeffries, R., Kern, J., Marick, B., Martin, R. C., Mellor, S., Schwaber, K., Sutherland, J., and Thomas, D. (2001). Manifesto for agile software development.
Boehm, B. W. (1981). Software Engineering Economics. Prentice Hall PTR, Upper Saddle River, NJ, USA, 1st edition.
Conte, S. D., Dunsmore, H. E., and Shen, V. Y. (1986). Software Engineering Metrics and Models. Benjamin-Cummings Publishing Co., Inc., Redwood City, CA, USA.
Haykin, S. (1998). Neural Networks: A Comprehensive Foundation. Prentice Hall PTR,Upper Saddle River, NJ, USA, 2nd edition.
Jones, C. (1986). Programming Productivity. McGraw-Hill Series in Software Enginee-ring & Technology. McGraw-Hill.
Jones, C. (1991). Applied Software Measurement: Assuring Productivity and Quality. McGraw-Hill, Inc., New York, NY, USA.
Kemerer, C. F. (1987). An empirical validation of software cost estimation models. Communications of the ACM, 30(5):416–429.
Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1):59–69.
Kumar, J., Rao, T., Babu, Y., Chaitanya, S., and Subrahmanyam, K. (2011). A novel method for software effort estimation using inverse regression as firing interval infuzzy logic. In 2011 3rd International Conference on Electronics Computer Technology (ICECT), volume 4, pages 177–182.
Manapian, A. and Prompoon, N. (2014). Software time estimation model for requirements change based on software prototype profiles using an analogy estimation method. In Computer Science and Engineering Conference (ICSEC), 2014 International, pages 366–371.
McCabe, T. J. (1976). A complexity measure. IEEE Transactions on Software Engineering, 2(4):308–320.
Pressman, R. (2010). Software Engineering: A Practitioner’s Approach. McGraw-Hill, Inc., New York, NY, USA, 7 edition.
Putnam, L. (1978). A general empirical solution to the macro software sizing and estimating problem. Software Engineering, IEEE Transactions on, SE-4(4):345–361.
Rastogi, H., Dhankhar, S., and Kakkar, M. (2014). A survey on software effort estimation techniques. In 2014 5th International Conference Confluence The Next Generation Information Technology Summit (Confluence), pages 826–830.
Satapathy, S., Kumar, M., and Rath, S. (2013). Class point approach for software effort estimation using soft computing techniques. In 2013 International Conference onAdvances in Computing, Communications and Informatics (ICACCI), pages 178–183.
Saxena, U. and Singh, S. (2012). Software effort estimation using neuro-fuzzy approach. In 2012 CSI Sixth International Conference on Software Engineering (CONSEG), pages 1–6.
Schwaber, K. and Beedle, M. (2001). Agile Software Development with Scrum. Prentice Hall PTR, Upper Saddle River, NJ, USA, 1st edition.
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, PROMISE ’14, pages 82–91, New York, NY, USA. ACM.
Attarzadeh, I. and Ow, S. H. (2011). Improving estimation accuracy of the cocomo ii using an adaptive fuzzy logic model. In 2011 IEEE International Conference on Fuzzy Systems (FUZZ), pages 2458–2464.
Beck, K., Beedle, M., van Bennekum, A., Cockburn, A., Cunningham, W., Fowler, M., Grenning, J., Highsmith, J., Hunt, A., Jeffries, R., Kern, J., Marick, B., Martin, R. C., Mellor, S., Schwaber, K., Sutherland, J., and Thomas, D. (2001). Manifesto for agile software development.
Boehm, B. W. (1981). Software Engineering Economics. Prentice Hall PTR, Upper Saddle River, NJ, USA, 1st edition.
Conte, S. D., Dunsmore, H. E., and Shen, V. Y. (1986). Software Engineering Metrics and Models. Benjamin-Cummings Publishing Co., Inc., Redwood City, CA, USA.
Haykin, S. (1998). Neural Networks: A Comprehensive Foundation. Prentice Hall PTR,Upper Saddle River, NJ, USA, 2nd edition.
Jones, C. (1986). Programming Productivity. McGraw-Hill Series in Software Enginee-ring & Technology. McGraw-Hill.
Jones, C. (1991). Applied Software Measurement: Assuring Productivity and Quality. McGraw-Hill, Inc., New York, NY, USA.
Kemerer, C. F. (1987). An empirical validation of software cost estimation models. Communications of the ACM, 30(5):416–429.
Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1):59–69.
Kumar, J., Rao, T., Babu, Y., Chaitanya, S., and Subrahmanyam, K. (2011). A novel method for software effort estimation using inverse regression as firing interval infuzzy logic. In 2011 3rd International Conference on Electronics Computer Technology (ICECT), volume 4, pages 177–182.
Manapian, A. and Prompoon, N. (2014). Software time estimation model for requirements change based on software prototype profiles using an analogy estimation method. In Computer Science and Engineering Conference (ICSEC), 2014 International, pages 366–371.
McCabe, T. J. (1976). A complexity measure. IEEE Transactions on Software Engineering, 2(4):308–320.
Pressman, R. (2010). Software Engineering: A Practitioner’s Approach. McGraw-Hill, Inc., New York, NY, USA, 7 edition.
Putnam, L. (1978). A general empirical solution to the macro software sizing and estimating problem. Software Engineering, IEEE Transactions on, SE-4(4):345–361.
Rastogi, H., Dhankhar, S., and Kakkar, M. (2014). A survey on software effort estimation techniques. In 2014 5th International Conference Confluence The Next Generation Information Technology Summit (Confluence), pages 826–830.
Satapathy, S., Kumar, M., and Rath, S. (2013). Class point approach for software effort estimation using soft computing techniques. In 2013 International Conference onAdvances in Computing, Communications and Informatics (ICACCI), pages 178–183.
Saxena, U. and Singh, S. (2012). Software effort estimation using neuro-fuzzy approach. In 2012 CSI Sixth International Conference on Software Engineering (CONSEG), pages 1–6.
Schwaber, K. and Beedle, M. (2001). Agile Software Development with Scrum. Prentice Hall PTR, Upper Saddle River, NJ, USA, 1st edition.
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, PROMISE ’14, pages 82–91, New York, NY, USA. ACM.
Publicado
17/08/2015
Como Citar
LIRA, Werney Ayala Luz; ALVES, Francisco Vanderson de Moura; DOS SANTOS NETO, Pedro de Alcântara; RABELO, Ricardo de Andrade Lira; BRITTO, Ricardo de Sousa.
Estimativa de Esforço em Projetos Ágeis de Software Utilizando Mapas de Kohonen. In: SIMPÓSIO BRASILEIRO DE QUALIDADE DE SOFTWARE (SBQS), 14. , 2015, Manaus.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2015
.
p. 19-33.
DOI: https://doi.org/10.5753/sbqs.2015.15211.
