Métrica de Coesão de Responsabilidade - A Utilidade de Métrica de Coesão na Identificação de Classes com Problemas Estruturais
Resumo
Muitas métricas de coesão de classe têm sido propostas na literatura. Entretanto, ainda não ha um consenso sobre a melhor abordagem para medir coesão. Uma questão importante nesse tópico e que o grau de coesão interna de uma classe e dificilmente capturado por meio automático, pois esse tipo de avaliação e estreitamente dependente do conhecimento do domínio de problema da aplicação. Os resultados relatados neste artigo identificam evidências de que, embora métricas de coesão possam não ser indicadores precisos, elas são úteis para a avaliação da qualidade estrutural de uma classe. São avaliadas quatro métricas: LCOM, LCOM4, TCC e COR. Coesão de Responsabilidade (COR) e definida neste trabalho como um indicador do número de responsabilidades implementadas por uma classe. A métrica COR foi definida de maneira a tornar mais simples a interpretação dos resultados da avaliação da coesão interna de uma classe. Os resultados deste trabalho mostram que a aplicação de métricas como COR podem auxiliar a identificar classes com problemas estruturais.
Palavras-chave:
Métricas de Coesão, Identificação de Classes, Problemas Estruturais
Referências
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Fowler, M. (1999). Refactoring - Improving the Design of Existing Code. Addison Wesley.
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Jancke, S. (2010). Smell Detection in Context. Diploma thesis. University of Bonn. Bonn, Germany., http://dirkriehle.com/computer-science/research/dissertation/.
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Marinescu, R. (1998). Using object-oriented metrics for automatic design flaws detection in large scale systems. In Workshop ion on Object-Oriented Technology, ECOOP ’98, pages 252–255, London, UK. Springer-Verlag.
Makel a, S. and Lepp anen, V. (2007). Cliente based object-oriented cohesion metrics. In IEEE 31st Annual International Computer Software and Applications Conference, pages 743–748, Beijing.
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Olbrich, S., Cruzes, D. S., Basili, V., and Zazworka, N. (2009). The evolution and impact of code smells: A case study of two open source systems. In ESEM ’09: Proceed ings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement, pages 390–400, Washington, DC, Estados Unidos. IEEE Computer Society.
Pressman, R. S. (2006). Engenharia de Software. MacGraw Hill, Rio de Janeiro, 6 edition.
Riehle, D. (2000). Framework Design: A Role Modeling Approach. Dissertation No. 13509, ETH Zurich., http://dirkriehle.com/computer-science/research/dissertation/.
Singh, Y., Kaur, A., and Malhotra, R. (2010). Empirical validation of object-oriented metrics for predicting fault proneness models. Software Quality Journal, 18:3–35. 10.1007/s11219-009-9079-6.
Zhou, Y. and Leung, H. (2006). Empirical analysis of object-oriented design metrics for predicting high and low severity faults. IEEE Transactions on Software Engineering, 32(10):771–789.
Al-Dallal, J. (2009). Software similarity-based functional cohesion metric. IET Software, 3(1):46–57.
Basili, V. R., Briand, L. C., and Melo, W. L. (1996). A validation of object-oriented design metrics as quality indicators. IEEE Trans. Softw. Eng., 22:751–761.
Bieman, J. M. and Kang, B.-K. (1995). Cohesion and reuse in an object-oriented system. SIGSOFT Softw. Eng. Notes, 20:259–262.
Briand, L. C., Daly, J. W., and Wust, J. (1998a). A unified framework for cohesion measurement in object-oriented systems. Empirical Softw. Eng., 3(1):65–117.
Briand, L. C., Wust, J., Daly, J., and Porter, V. (1998b). A comprehensive empirical validation of design measures for object-oriented systems. In Proceedings of the 5th International Symposium on Software Metrics, METRICS ’98, pages 246–, Washington, DC, USA. IEEE Computer Society.
Chidamber, S. R. and Kemerer, C. F. (1994). A metrics suite for object oriented design. IEEE Trans. Softw. Eng., 20:476–493.
Counsell, S., Swift, S., and Crampton, J. (2006). The interpretation and utility of three co hesion metrics for object-oriented design. ACM Trans. Softw. Eng. Methodol., 15:123–149.
Czibula, I. G. and Czibula, G. (2008). Clustering based automatic refactorings identification. In Proceedings of the 2008 10th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, pages 253–256, Washington, DC, Esta dos Unidos. IEEE Computer Society.
Ferreira, K. A. M. (2006). Avaliaçao de Conectividade em Sistemas Orientados por Objetos. Dissertaçao de Mestrado - DCC/UFMG., Belo Horizonte, Brasil.
Ferreira, K. A. M. (2011). Um Modelo de Predição de Amplitude da Propagação de Modificações Contratuais em Software Orientado por Objetos. Tese de Doutorado - DCC/UFMG., Belo Horizonte, Brasil.
Ferreira, K. A. M., Bigonha, M. A. S., Bigonha, R., Mendes, L. F. O., and Almeida, H. C. (2009). Reference values for object-oriented software. In XXIII Brazilian Symposium on Software Engineering, pages 62–72, Fortaleza, Ceara, Brazil.
Fowler, M. (1999). Refactoring - Improving the Design of Existing Code. Addison Wesley.
Gyimothy, T., Ferenc, R., and Siket, I. (2005). Empirical validation of object-oriented metrics on open source software for fault prediction. IEEE Transactions on Software Engineering, 31:897–910.
Hitz, M. and Montazeri, B. (1995). Measuring coupling and cohesion in object-oriented systems. In Int. Symposium on Applied Corporate Computing, Monterrey, Mexico.
Jancke, S. (2010). Smell Detection in Context. Diploma thesis. University of Bonn. Bonn, Germany., http://dirkriehle.com/computer-science/research/dissertation/.
Kessentini, M., Vaucher, S., and Sahraoui, H. (2010). Deviance from perfection is a better criterion than closeness to evil when identifying risky code. In Proceedings of the IEEE/ACM International conference on Automated Software Engineering, ASE ’10, pages 113–122, New York, NY, Estados Unidos. ACM.
Kitchenham, B. (2009). What’s up with software metrics? - a preliminary mapping study. The Journal of Systems and Software, 83:37–51.
Lincke, R., Lundberg, J., and Lowe, W. (2008). Comparing software metrics tools. In ISSTA ’08: Proceedings of the 2008 International Symposium on Software Testing and Analysis, pages 131–142, New York, NY, Estados Unidos. ACM.
Marcus, A. and Poshyvanyk, D. (2005). The conceptual cohesion of classes. In Proceedings of the 21st IEEE International Conference on Software Maintenance, pages 133–142, Washington, DC, Estados Unidos. IEEE Computer Society.
Marinescu, R. (1998). Using object-oriented metrics for automatic design flaws detection in large scale systems. In Workshop ion on Object-Oriented Technology, ECOOP ’98, pages 252–255, London, UK. Springer-Verlag.
Makel a, S. and Lepp anen, V. (2007). Cliente based object-oriented cohesion metrics. In IEEE 31st Annual International Computer Software and Applications Conference, pages 743–748, Beijing.
Myers, G. J. (1975). Reliable software through composite design. Petrocelli/Charter, Nova York, 2 edition.
Olague, H. M., Etzkorn, L. H., Gholston, S., and Quattlebaum, S. (2007). Empirical validation of three software metrics suites to predict fault-proneness of object-oriented classes developed using highly iterative or agile software development processes. IEEE Trans. Softw. Eng., 33:402–419.
Olbrich, S., Cruzes, D. S., Basili, V., and Zazworka, N. (2009). The evolution and impact of code smells: A case study of two open source systems. In ESEM ’09: Proceed ings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement, pages 390–400, Washington, DC, Estados Unidos. IEEE Computer Society.
Pressman, R. S. (2006). Engenharia de Software. MacGraw Hill, Rio de Janeiro, 6 edition.
Riehle, D. (2000). Framework Design: A Role Modeling Approach. Dissertation No. 13509, ETH Zurich., http://dirkriehle.com/computer-science/research/dissertation/.
Singh, Y., Kaur, A., and Malhotra, R. (2010). Empirical validation of object-oriented metrics for predicting fault proneness models. Software Quality Journal, 18:3–35. 10.1007/s11219-009-9079-6.
Zhou, Y. and Leung, H. (2006). Empirical analysis of object-oriented design metrics for predicting high and low severity faults. IEEE Transactions on Software Engineering, 32(10):771–789.
Publicado
06/06/2011
Como Citar
FERREIRA, Kecia A. M.; BIGONHA, Mariza A. S.; BIGONHA, Roberto S.; ALMEIDA, Heitor C.; NEVES, Roberta Coeli das.
Métrica de Coesão de Responsabilidade - A Utilidade de Métrica de Coesão na Identificação de Classes com Problemas Estruturais. In: SIMPÓSIO BRASILEIRO DE QUALIDADE DE SOFTWARE (SBQS), 10. , 2011, Curitiba.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2011
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p. 9-23.
DOI: https://doi.org/10.5753/sbqs.2011.15384.
