Uma Metodologia para Desenvolvimento de Modelos de Desempenho de Processos para Gerência Quantitativa de Projetos de Software
Resumo
O controle estatístico de processos tem sido utilizado para controlar o desempenho de processos de software. Esta técnica se limita a controlar o valor de uma métrica em um projeto atual em relação a esta mesma métrica considerando o desempenho do processo. Este artigo apresenta uma metodologia para a gerência quantitativa de projetos de software combinando o controle estatístico de processos com modelos estatísticos de desempenho possibilitando que possam ser feitas previsões sobre diferentes cenários, apoiando a tomada de decisões gerenciais. A metodologia foi desenvolvida durante a aplicação destas técnicas em uma organização de software para controlar e relacionar os processos Garantia da Qualidade e Verificação.
Palavras-chave:
Gerência Quantitativa, Modelos de Desempenho, Metodologia de Desenvolvimento
Referências
Crissis, M.B., Konrad, M., Shrum, S. (2006), CMMI - Guidelines for Process Integration and Product Improvement (2nd Edition), SEI Series in Software Engineering, Addison-Wesly Professional.
D'Agostino, Stephens (1986), Goodness-Of-Fit Techniques, Marcel-Dekker, New York, Table 4.7, p.123. All of Chapter 4, pp.97-193, deals with goodness-of-fit tests based on empirical distribution function (EDF) statistics
Fairley, R.E. (1999), “Managing by the Numbers: A tutorial on quantitative measurement and control of software projects”, in: International Conference on Software Engineering, ICSE'99, Los Angeles, CA.
Fenton, N.E. (1999), “A Critique of Software Defect Prediction Models”, IEEE Transactions on Software Engineering, vol 25, no 5.
Florac, W.A., Carleton, A.D. (1999), Measuring the Software Process — Statistical Process Control for Software Process Improvement, Addison-Wesley.
Hong, G.Y., Xie, M., Shanmugan, P. (1999), A Statistical Method For Controlling Software Defect Detection Process, J. on Comp. and Industrial Engineering, vol. 37.
Maxwell, K.D. (2002), Applied Statistics for Software Managers, Software Quality Institute Series, Prentice-Hall.
MPS.BR — Melhoria de Processo do Software Brasileiro, Guia Geral v1.1, (2006), Maio, http://www.softex.br/mpsbr/_guias/MPS.BR Guia Geral V1.1.pdf.
Munson, J.C., Khoshgoftaar, T.M. (1990) “Regression Modelling of Software Quality: An Empirical Investigation,” Information and Software Technology, vol. 32, no. 2, pp. 106-114, 1990.
Shewhart, W.A. (1931), Economic Control of Quality of Manufactured Products, Van Nostrand, New York.
Solingen, R., Berghout, E. (1999), The Goal/Question/Metric Method — A Practical Guide for Quality Improvement of Software Development, McGrawHIill.
Wang, Q., Jiang, N., Gou, L., Liu, X., Li, M., Wang, Y. (2006), BSR: A Statistic-based Approach for Establishing and Refining Software Process Performance Baseline, in: Int. Conf. on Soft. Eng., ICSE'06, ACM Press, New York, NY, USA, pp. 585-594.
Wheeler, Donald J. (1999). Understanding Variation: The Key to Managing Chaos, 2nd Edition. SPC Press.
D'Agostino, Stephens (1986), Goodness-Of-Fit Techniques, Marcel-Dekker, New York, Table 4.7, p.123. All of Chapter 4, pp.97-193, deals with goodness-of-fit tests based on empirical distribution function (EDF) statistics
Fairley, R.E. (1999), “Managing by the Numbers: A tutorial on quantitative measurement and control of software projects”, in: International Conference on Software Engineering, ICSE'99, Los Angeles, CA.
Fenton, N.E. (1999), “A Critique of Software Defect Prediction Models”, IEEE Transactions on Software Engineering, vol 25, no 5.
Florac, W.A., Carleton, A.D. (1999), Measuring the Software Process — Statistical Process Control for Software Process Improvement, Addison-Wesley.
Hong, G.Y., Xie, M., Shanmugan, P. (1999), A Statistical Method For Controlling Software Defect Detection Process, J. on Comp. and Industrial Engineering, vol. 37.
Maxwell, K.D. (2002), Applied Statistics for Software Managers, Software Quality Institute Series, Prentice-Hall.
MPS.BR — Melhoria de Processo do Software Brasileiro, Guia Geral v1.1, (2006), Maio, http://www.softex.br/mpsbr/_guias/MPS.BR Guia Geral V1.1.pdf.
Munson, J.C., Khoshgoftaar, T.M. (1990) “Regression Modelling of Software Quality: An Empirical Investigation,” Information and Software Technology, vol. 32, no. 2, pp. 106-114, 1990.
Shewhart, W.A. (1931), Economic Control of Quality of Manufactured Products, Van Nostrand, New York.
Solingen, R., Berghout, E. (1999), The Goal/Question/Metric Method — A Practical Guide for Quality Improvement of Software Development, McGrawHIill.
Wang, Q., Jiang, N., Gou, L., Liu, X., Li, M., Wang, Y. (2006), BSR: A Statistic-based Approach for Establishing and Refining Software Process Performance Baseline, in: Int. Conf. on Soft. Eng., ICSE'06, ACM Press, New York, NY, USA, pp. 585-594.
Wheeler, Donald J. (1999). Understanding Variation: The Key to Managing Chaos, 2nd Edition. SPC Press.
Publicado
01/06/2007
Como Citar
MONTONI, Mariano; KALINOWSKI, Marcos; LUPO, Peter; ABRANTES, José Fortuna; FERREIRA, Analia Irigoyen Ferreiro; ROCHA, Ana Regina.
Uma Metodologia para Desenvolvimento de Modelos de Desempenho de Processos para Gerência Quantitativa de Projetos de Software. In: SIMPÓSIO BRASILEIRO DE QUALIDADE DE SOFTWARE (SBQS), 6. , 2007, Porto de Galinhas.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2007
.
p. 325-339.
DOI: https://doi.org/10.5753/sbqs.2007.15585.
