Uma Investigação de Modelos de Estimativas de Esforço em Gerenciamento de Projeto de Software
Resumo
Estimativas precisas em gerenciamento de projetos é um fator crítico. Medidas de tamanho, esforço, recursos, custo, e tempo despendidos no desenvolvimento de software são fundamentais. Valores subestimados de esforço podem fazer com que pressões de tempo comprometam todo o desenvolvimento funcional e até mesmo o teste de software. Por outro lado, valores superestimados podem resultar em projetos não competitivos. Neste artigo, modelos como redes neurais e regressão, são apontados como alternativas para aqueles que não acreditam em modelos de estimativas. Os resultados apresentados comparam o desempenho desses métodos e indicam que estas técnicas são competitivas com os métodos APF, SLIM e COCOMO.
Referências
Agarval, R. (2001). Estimating software projects, Software Engineering Notes, v.26, p. 60-57.
Albrecht, A.J. (1979). Measuring application development productivity, Proc. IBM Application Development Symposium, p. 83-92.
Albrecht, A.J. and Gaffney, J.R. (1983). Software function, source lines of code and development effort prediction: a software science validation, IEEE Transactions on Software Engineering, v. 9, n. 6, p. 639-648.
Angelis, L. and Stamelos, I. (2000). A simulation tool for efficient analogy based cost estimation, Empirical Software Engineering, n.5, 35-68.
Barcelos Tronto, I. F., Simões da Silva, J.D. and Sant’Anna, N. (2006). Comparison of Artificial Neural Network and Regression Models in Software Effort Estimation, In: Symposium Brazilian of Neural Networks, Ribeirão Preto, Brazil (submitted).
Bisio, R. and Malabocchia, F. (1995). Cost estimation of software projects through case base reasoning, 1st Intl. Conf. on Case-Based Reasoning Research & Development, Springer-Verlag, p.11-22.
Boehm, B.W. (1981). Software engineering economics, Prentice-Hall, Englewood Cliffs, NJ.
Boehm, W., Horowitz, E., Madachy, R., Reifer, D., Clark, B.K., Steece, B., Brown, A.D. and Abts, C. (2000). Software Cost Estimation with COCOMOII, Prentice-Hall.
Briand, L.C., Langley, T. and Wieczorek, I. (2000). A replicated assessment and comparison of common software cost modeling techniques, Pro. ICSE 2000, Limerick, Ireland, 377-386.
Briand, L.C. and Wieczorek, I. (2002). Software resource estimation, Encyclopedia of Software engineering, n.2, p. 1160-1196.
Finnie, G.R. Witting, G.E. and Desharnais, J-M. (1997). A comparison of software effort estimation techniques: Using function points with neural networks, case-based reasoning and regression models, Journal of Systems and Software, 39, 281-289.
Gray, A.R. and MacDonell, S.G. (1997). A comparison of model building techniques to develop predictive equations for software metrics. Information and Software Technology, 39, 425-437.
Gray, A.R., MacDonell, S.G. and Shepperd, M. (1999). Factors systematically associated with errors in subjective estimates of software development effort: the stability of expert judgment, Proc. IEEE 6th Metrics Symposium.
Hasting, T.E. and Sajeev, A.S.M. (2001). A vector based approach to software size measurement and effort estimation,” IEEE Transactions on Soft. Engineering, v. 27, n.4.
Jeffery, R., Ruhe, M. and Wieczorek, I. (2001). Using public domain metrics to estimate software development effort, Proc. IEEE 7th Metrics Symposium, London, UK, 16-27.
Jones, C. 1986. Estimating Software Costs, McGraw-Hill.
Karunanitthi, N., Whitley, D. and Malaiya, Y.K. (1992). Using neural networks in reliability prediction, IEEE Software, v. 9, n.4, p.53-59.
Kemerer, C.F. (1987). An empirical validation of software cost estimation models, Comunication of ACM, v.30, p.416-429.
Khoshgoftaar, T. M., Allen, E.B. and Xu, Z. (2000). Predicting testability of program modules using a neural network, Proc. 3rd IEEE Symposium on Application-Specific Systems and Sof. Eng. Technology, p. 57-62.
Kitchenham, B. (1998). A procedure for analyzing unbalanced datasets, IEEE Transactions on Software Engineering, 24, 278-301.
Kok, P., Kitchenham, B.A. and Kirakowski, J. (1990). The MERMAID approach to software cost estimation, Espirit Technical Week.
Kumar, S. Krishna, B.A. and Satsangi, P.S. (1994). Fuzzy systems and neural networks in software engineering project management. Journal of Applied Intelligence, 4: 31-52.
Lai, R. and Huang, S. (2003). A model for estimating the size of a formal communication protocol specification and its implementation, IEEE Transaction on Software Engineering, v.29, n. 1, p. 46-62.
MCT Ministerio da Ciência e Tecnologia, (2001). Qualidade e Produtividade no setor de software,” In: [link], Tabela 40 – Práticas de Engenharia de Software no Desenv. e Manutenção de Software.
Myrtveit, I., Stensrud, E. and Shepperd, M. (2005). Reliability and validity in comparative studies of software prediction models, IEEE Transaction on Soft. Engineering, v.31, n. 5, p. 46-62.
Putnam, L.H.(1978). A general empirical solution to the Macro Sizing and Estimating Problem, IEEE Transaction on Software Engineering, 4, 345-361.
Samson, B., Ellison, D. and Dugard, P. (1997). Software cost estimation using albus perceptron (CMAC), Information and Software Technology, v.39, p. 55-60.
Selby, R.W. and Porter, A.A. (1998). Learning from examples: generation and evaluation of decision trees for software resource analysis. IEEE Trans on Software Engineering, 14, 1743-1757.
Sentas, P., Angelis, L., Stamelos, I. and Bleris, G. (2005). Software productivity and effort prediction with ordinal regression, Journal Information and Software Technology, n. 47, p.17-29.
Shepeerd, M.J., Shofield, C. and Kitchenham, B. (1996). Effort Estimation Using Analogy. Proc. ICSE-18, Berlin.
Shepperd, M. and Schofield, C. (1997). Estimating software project effort using analogies, IEEE Transactions on Software Engineering, v.23, n.12, p.736-743.
Srinivazan, K. and Fisher, D. (1995). Machine learning approaches to estimating software development effort, IEEE Transactions on Software Engineering, v.21, n.2, p.126-137.
Vicinanza, S., Prietula, M.J. and Mukhopadhyay, T. (1990). Case-based reasoning in software effort estimation,” In Proc.11th Int. Conf. Info. Syst, p.149-158.
Zhong, S., Khoshgoftaar, T.M. and Seliya, N. (2004). Analysing software measurement data with clustering techniques, IEEE Intelligent Systems, p.20-27.
Witting, G. and Finnie, G. (1994). Using artificial neural networks and function points to estimate 4GL software development effort, Journal Information and Systems, v. 1, n.2, p. 87-94.
Witting, G. and Finnie, G. (1997). Estimating software development effort with connectionist models, Information and Software Technology, v. 39, p. 369-476.
Albrecht, A.J. (1979). Measuring application development productivity, Proc. IBM Application Development Symposium, p. 83-92.
Albrecht, A.J. and Gaffney, J.R. (1983). Software function, source lines of code and development effort prediction: a software science validation, IEEE Transactions on Software Engineering, v. 9, n. 6, p. 639-648.
Angelis, L. and Stamelos, I. (2000). A simulation tool for efficient analogy based cost estimation, Empirical Software Engineering, n.5, 35-68.
Barcelos Tronto, I. F., Simões da Silva, J.D. and Sant’Anna, N. (2006). Comparison of Artificial Neural Network and Regression Models in Software Effort Estimation, In: Symposium Brazilian of Neural Networks, Ribeirão Preto, Brazil (submitted).
Bisio, R. and Malabocchia, F. (1995). Cost estimation of software projects through case base reasoning, 1st Intl. Conf. on Case-Based Reasoning Research & Development, Springer-Verlag, p.11-22.
Boehm, B.W. (1981). Software engineering economics, Prentice-Hall, Englewood Cliffs, NJ.
Boehm, W., Horowitz, E., Madachy, R., Reifer, D., Clark, B.K., Steece, B., Brown, A.D. and Abts, C. (2000). Software Cost Estimation with COCOMOII, Prentice-Hall.
Briand, L.C., Langley, T. and Wieczorek, I. (2000). A replicated assessment and comparison of common software cost modeling techniques, Pro. ICSE 2000, Limerick, Ireland, 377-386.
Briand, L.C. and Wieczorek, I. (2002). Software resource estimation, Encyclopedia of Software engineering, n.2, p. 1160-1196.
Finnie, G.R. Witting, G.E. and Desharnais, J-M. (1997). A comparison of software effort estimation techniques: Using function points with neural networks, case-based reasoning and regression models, Journal of Systems and Software, 39, 281-289.
Gray, A.R. and MacDonell, S.G. (1997). A comparison of model building techniques to develop predictive equations for software metrics. Information and Software Technology, 39, 425-437.
Gray, A.R., MacDonell, S.G. and Shepperd, M. (1999). Factors systematically associated with errors in subjective estimates of software development effort: the stability of expert judgment, Proc. IEEE 6th Metrics Symposium.
Hasting, T.E. and Sajeev, A.S.M. (2001). A vector based approach to software size measurement and effort estimation,” IEEE Transactions on Soft. Engineering, v. 27, n.4.
Jeffery, R., Ruhe, M. and Wieczorek, I. (2001). Using public domain metrics to estimate software development effort, Proc. IEEE 7th Metrics Symposium, London, UK, 16-27.
Jones, C. 1986. Estimating Software Costs, McGraw-Hill.
Karunanitthi, N., Whitley, D. and Malaiya, Y.K. (1992). Using neural networks in reliability prediction, IEEE Software, v. 9, n.4, p.53-59.
Kemerer, C.F. (1987). An empirical validation of software cost estimation models, Comunication of ACM, v.30, p.416-429.
Khoshgoftaar, T. M., Allen, E.B. and Xu, Z. (2000). Predicting testability of program modules using a neural network, Proc. 3rd IEEE Symposium on Application-Specific Systems and Sof. Eng. Technology, p. 57-62.
Kitchenham, B. (1998). A procedure for analyzing unbalanced datasets, IEEE Transactions on Software Engineering, 24, 278-301.
Kok, P., Kitchenham, B.A. and Kirakowski, J. (1990). The MERMAID approach to software cost estimation, Espirit Technical Week.
Kumar, S. Krishna, B.A. and Satsangi, P.S. (1994). Fuzzy systems and neural networks in software engineering project management. Journal of Applied Intelligence, 4: 31-52.
Lai, R. and Huang, S. (2003). A model for estimating the size of a formal communication protocol specification and its implementation, IEEE Transaction on Software Engineering, v.29, n. 1, p. 46-62.
MCT Ministerio da Ciência e Tecnologia, (2001). Qualidade e Produtividade no setor de software,” In: [link], Tabela 40 – Práticas de Engenharia de Software no Desenv. e Manutenção de Software.
Myrtveit, I., Stensrud, E. and Shepperd, M. (2005). Reliability and validity in comparative studies of software prediction models, IEEE Transaction on Soft. Engineering, v.31, n. 5, p. 46-62.
Putnam, L.H.(1978). A general empirical solution to the Macro Sizing and Estimating Problem, IEEE Transaction on Software Engineering, 4, 345-361.
Samson, B., Ellison, D. and Dugard, P. (1997). Software cost estimation using albus perceptron (CMAC), Information and Software Technology, v.39, p. 55-60.
Selby, R.W. and Porter, A.A. (1998). Learning from examples: generation and evaluation of decision trees for software resource analysis. IEEE Trans on Software Engineering, 14, 1743-1757.
Sentas, P., Angelis, L., Stamelos, I. and Bleris, G. (2005). Software productivity and effort prediction with ordinal regression, Journal Information and Software Technology, n. 47, p.17-29.
Shepeerd, M.J., Shofield, C. and Kitchenham, B. (1996). Effort Estimation Using Analogy. Proc. ICSE-18, Berlin.
Shepperd, M. and Schofield, C. (1997). Estimating software project effort using analogies, IEEE Transactions on Software Engineering, v.23, n.12, p.736-743.
Srinivazan, K. and Fisher, D. (1995). Machine learning approaches to estimating software development effort, IEEE Transactions on Software Engineering, v.21, n.2, p.126-137.
Vicinanza, S., Prietula, M.J. and Mukhopadhyay, T. (1990). Case-based reasoning in software effort estimation,” In Proc.11th Int. Conf. Info. Syst, p.149-158.
Zhong, S., Khoshgoftaar, T.M. and Seliya, N. (2004). Analysing software measurement data with clustering techniques, IEEE Intelligent Systems, p.20-27.
Witting, G. and Finnie, G. (1994). Using artificial neural networks and function points to estimate 4GL software development effort, Journal Information and Systems, v. 1, n.2, p. 87-94.
Witting, G. and Finnie, G. (1997). Estimating software development effort with connectionist models, Information and Software Technology, v. 39, p. 369-476.
Publicado
16/10/2006
Como Citar
TRONTO, Iris Fabiana de Barcelos; SILVA, José Demísio Simões da; SANT’ANNA, Nilson.
Uma Investigação de Modelos de Estimativas de Esforço em Gerenciamento de Projeto de Software. In: SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SOFTWARE (SBES), 20. , 2006, Florianópolis.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2006
.
p. 224-238.
DOI: https://doi.org/10.5753/sbes.2006.21215.
