Medidas para Avaliação da Manutenibilidade do Modelo de Features de Linhas de Produto de Software Tradicionais e Dinâmicas
Resumo
Este trabalho tem por objetivo investigar a manutenibilidade e propor soluções para avaliar o modelo de features utilizando medidas. Para atingir esse objetivo, foi elaborado um catálogo de medidas de qualidade de manutenibilidade, e para apoiar o uso do catálogo, foi desenvolvida uma ferramenta, que permite a coleta automática de medições pertencentes a este catálogo. Para validação foi realizado um estudo exploratório que investiga o impacto da evolução dos modelos de features na manutenibilidade; um estudo de caso exploratório efetuado com o intuito de explorar os relacionamentos entre as medidas de manutenibilidade; e um estudo com o propósito de agregar medidas, relacionadas à LPSDs, por meio da utilização de lógica fuzzy. Os resultados desta tese sugerem que as medidas de qualidade podem ser efetivamente utilizadas para apoiar a avaliação da manutenibilidade de modelos de features
Referências
Alférez, G. H., Pelechano, V., Mazo, R., Salinesi, C., and Diaz, D. (2014). Dynamic adaptation of service compositions with variability models. Journal of Systems and Software, 91:24–47.
Altman, D., Machin, D., Bryant, T., and Gardner, M. (2013). Statistics with confidence: confidence intervals and statistical guidelines. John Wiley & Sons.
Asadi, M., Gröner, G., Mohabbati, B., and Gasevic, D. (2016). Goal-oriented modeling and verification of feature-oriented product lines. Software & Systems Modeling, 15(1):257–279.
Bagheri, E. e Gasevic, D. (2011). Assessing the maintainability of software product line feature models using structural metrics. Software Quality Control, 19(3):579–612.
Benavides, D., Segura, S., and Ruiz-Cortés, A. (2010). Automated analysis of feature models 20 years later: A literature review. Information Systems, 35(6):615–636.
Benavides Cuevas, D. F., Segura Rueda, S., Trinidad Martín Arroyo, P., and Ruiz Cortés, A. (2007). Fama: Tooling a framework for the automated analysis of feature models.
Berger, T. e Guo, J. (2014). Towards system analysis with variability model metrics. In Proceedings of the Eighth International Workshop on Variability Modelling of Software-Intensive Systems, page 23. ACM.
Bezerra, C. I., Andrade, R. M., and Monteiro, J. M. (2016a). Exploring quality measures for the evaluation of feature models: A case study. Journal of Systems and Software.
Bezerra, C. I., Barbosa, J., Freires, J. H., Andrade, R., and Monteiro, J. M. (2016b). Dymmer: a measurement-based tool to support quality evaluation of dspl feature models. In Proceedings of the 20th International Systems and Software Product Line Conference, pages 314–317. ACM.
Bezerra, C. I., Monteiro, J. M., Andrade, R., and Rocha, L. S. (2016c). Analyzing the feature models maintainability over their evolution process: An exploratory study. In Proceedings of the Tenth International Workshop on Variability Modelling of Softwareintensive Systems, pages 17–24. ACM.
Bezerra, C. I., Monteiro, J. M. S., and Andrade, R. (2013). Avaliação da qualidade do modelo de Features em linhas de produto de software utilizando medidas. In Simpósio Brasileiro de Qualidade de Software, page 15.
Bezerra, C. I. M., Andrade, R. M. C., and Monteiro, J. M. (2015). Measures for quality evaluation of feature models. In Software Reuse for Dynamic Systems in the Cloud and Beyond 14th International Conference on Software Reuse, ICSR 2015, Miami, FL, USA, January 4-6, 2015. Proceedings, pages 282–297.
Capilla, R. and Bosch, J. (2011). The promise and challenge of runtime variability. Computer, 44(12):93–95.
Capilla, R., Bosch, J., and Kang, K.-C. (2013). Systems and software variability management. Springer.
Capilla, R., Bosch, J., Trinidad, P., Ruiz-Cortés, A., and Hinchey, M. (2014). An overview of dynamic software product line architectures and techniques: Observations from research and industry. Journal of Systems and Software, 91:3–23.
Cetina, C., Giner, P., Fons, J., and Pelechano, V. (2009). Using feature models for developing self-configuring smart homes. In Autonomic and Autonomous Systems, 2009. ICAS’09. Fifth International Conference on, pages 179–188. IEEE.
Etxeberria, L. and Sagardui, G. (2008a). Quality assessment in software product lines. In High Confidence Software Reuse in Large Systems, pages 178–181. Springer.
Etxeberria, L. and Sagardui, G. (2008b). Variability driven quality evaluation in software product lines. In Software Product Line Conference, 2008. SPLC’08. 12th International, pages 243–252. IEEE.
Hallsteinsen, S., Hinchey, M., Park, S., and Schmid, K. (2008). Dynamic software product lines. Computer, 41(4):93–95.
ISO/IEC (2014). Iso/iec 25000 systems and software engineering – systems and software quality requirements and evaluation (square) – guide to square. Technical report.
Kim, T., Ko, I. Y., Kang, S. W., and Lee, D. H. (2008). Extending atam to assess product line architecture. In Computer and Information Technology. CIT 2008. 8th IEEE International Conference on, pages 790–797. IEEE.
Lochau, M., Bürdek, J., Hölzle, S., and Schürr, A. (2015). Specification and automated validation of staged reconfiguration processes for dynamic software product lines. Software & Systems Modeling, pages 1–28.
Marinho, F. G., Maia, P. H., Andrade, R., Vidal, V. M., Costa, P. A., and Werner, C. (2012). Safe adaptation in context-aware feature models. In Proceedings of the 4th International Workshop on Feature-Oriented Software Development, pages 54–61. ACM.
Mendonca, M., Branco, M., and Cowan, D. (2009). S.p.l.o.t.: Software product lines online tools. In Proceedings of the 24th ACM SIGPLAN Conference Companion on Object Oriented Programming Systems Languages and Applications, OOPSLA ’09, pages 761–762, New York, NY, USA. ACM.
Montagud, S., Abrahão, S., and Insfran, E. (2012). A systematic review of quality attributes and measures for software product lines. Software Quality Journal, 20(3-4):425– 486.
Montagud, S. and Abrahão, S. (2009). Gathering current knowledge about quality evaluation in software product lines. In Proceedings of the 13th International Software Product Line Conference, SPLC ’09, pages 91–100, Pittsburgh, PA, USA. Carnegie Mellon University.
Oliinyk, O., Petersen, K., Schoelzke, M., Becker, M., and Schneickert, S. (2015). Metrics for the evaluation of feature models in an industrial context: A case study at opel. In International Working Conference on Requirements Engineering: Foundation for Software Quality, pages 33–48. Springer.
Olumofin, F. G. and Misic, V. B. (2007). A holistic architecture assessment method for software product lines. Information and Software Technology, 49(4):309–323.
Pascual, G. G., Lopez-Herrejon, R. E., Pinto, M., Fuentes, L., and Egyed, A. (2015). Applying multiobjective evolutionary algorithms to dynamic software product lines for reconfiguring mobile applications. Journal of Systems and Software, 103:392–411.
Runeson, P. e Höst, M. (2009). Guidelines for conducting and reporting case study research in software engineering. Empirical software engineering, 14(2):131–164.
Saller, K., Lochau, M., and Reimund, I. (2013). Context-aware dspls: model-based runtime adaptation for resource-constrained systems. In Proceedings of the 17th International Software Product Line Conference co-located workshops, pages 106–113. ACM.
Thörn, C. (2010). On the quality of feature models.
Thüm, T., Apel, S., Kästner, C., Schaefer, I., and Saake, G. (2014). A classification and survey of analysis strategies for software product lines. ACM Computing Surveys (CSUR), 47(1):6.
Trinidad, P., Benavides, D., Durán, A., Ruiz-Cortés, A., and Toro, M. (2008). Automated error analysis for the agilization of feature modeling. Journal of Systems and Software, 81(6):883–896.
Wohlin, C., Runeson, P., Höst, M., Ohlsson, M. C., Regnell, B., and Wesslén, A. (2012). Experimentation in software engineering. Springer Science & Business Media.
Altman, D., Machin, D., Bryant, T., and Gardner, M. (2013). Statistics with confidence: confidence intervals and statistical guidelines. John Wiley & Sons.
Asadi, M., Gröner, G., Mohabbati, B., and Gasevic, D. (2016). Goal-oriented modeling and verification of feature-oriented product lines. Software & Systems Modeling, 15(1):257–279.
Bagheri, E. e Gasevic, D. (2011). Assessing the maintainability of software product line feature models using structural metrics. Software Quality Control, 19(3):579–612.
Benavides, D., Segura, S., and Ruiz-Cortés, A. (2010). Automated analysis of feature models 20 years later: A literature review. Information Systems, 35(6):615–636.
Benavides Cuevas, D. F., Segura Rueda, S., Trinidad Martín Arroyo, P., and Ruiz Cortés, A. (2007). Fama: Tooling a framework for the automated analysis of feature models.
Berger, T. e Guo, J. (2014). Towards system analysis with variability model metrics. In Proceedings of the Eighth International Workshop on Variability Modelling of Software-Intensive Systems, page 23. ACM.
Bezerra, C. I., Andrade, R. M., and Monteiro, J. M. (2016a). Exploring quality measures for the evaluation of feature models: A case study. Journal of Systems and Software.
Bezerra, C. I., Barbosa, J., Freires, J. H., Andrade, R., and Monteiro, J. M. (2016b). Dymmer: a measurement-based tool to support quality evaluation of dspl feature models. In Proceedings of the 20th International Systems and Software Product Line Conference, pages 314–317. ACM.
Bezerra, C. I., Monteiro, J. M., Andrade, R., and Rocha, L. S. (2016c). Analyzing the feature models maintainability over their evolution process: An exploratory study. In Proceedings of the Tenth International Workshop on Variability Modelling of Softwareintensive Systems, pages 17–24. ACM.
Bezerra, C. I., Monteiro, J. M. S., and Andrade, R. (2013). Avaliação da qualidade do modelo de Features em linhas de produto de software utilizando medidas. In Simpósio Brasileiro de Qualidade de Software, page 15.
Bezerra, C. I. M., Andrade, R. M. C., and Monteiro, J. M. (2015). Measures for quality evaluation of feature models. In Software Reuse for Dynamic Systems in the Cloud and Beyond 14th International Conference on Software Reuse, ICSR 2015, Miami, FL, USA, January 4-6, 2015. Proceedings, pages 282–297.
Capilla, R. and Bosch, J. (2011). The promise and challenge of runtime variability. Computer, 44(12):93–95.
Capilla, R., Bosch, J., and Kang, K.-C. (2013). Systems and software variability management. Springer.
Capilla, R., Bosch, J., Trinidad, P., Ruiz-Cortés, A., and Hinchey, M. (2014). An overview of dynamic software product line architectures and techniques: Observations from research and industry. Journal of Systems and Software, 91:3–23.
Cetina, C., Giner, P., Fons, J., and Pelechano, V. (2009). Using feature models for developing self-configuring smart homes. In Autonomic and Autonomous Systems, 2009. ICAS’09. Fifth International Conference on, pages 179–188. IEEE.
Etxeberria, L. and Sagardui, G. (2008a). Quality assessment in software product lines. In High Confidence Software Reuse in Large Systems, pages 178–181. Springer.
Etxeberria, L. and Sagardui, G. (2008b). Variability driven quality evaluation in software product lines. In Software Product Line Conference, 2008. SPLC’08. 12th International, pages 243–252. IEEE.
Hallsteinsen, S., Hinchey, M., Park, S., and Schmid, K. (2008). Dynamic software product lines. Computer, 41(4):93–95.
ISO/IEC (2014). Iso/iec 25000 systems and software engineering – systems and software quality requirements and evaluation (square) – guide to square. Technical report.
Kim, T., Ko, I. Y., Kang, S. W., and Lee, D. H. (2008). Extending atam to assess product line architecture. In Computer and Information Technology. CIT 2008. 8th IEEE International Conference on, pages 790–797. IEEE.
Lochau, M., Bürdek, J., Hölzle, S., and Schürr, A. (2015). Specification and automated validation of staged reconfiguration processes for dynamic software product lines. Software & Systems Modeling, pages 1–28.
Marinho, F. G., Maia, P. H., Andrade, R., Vidal, V. M., Costa, P. A., and Werner, C. (2012). Safe adaptation in context-aware feature models. In Proceedings of the 4th International Workshop on Feature-Oriented Software Development, pages 54–61. ACM.
Mendonca, M., Branco, M., and Cowan, D. (2009). S.p.l.o.t.: Software product lines online tools. In Proceedings of the 24th ACM SIGPLAN Conference Companion on Object Oriented Programming Systems Languages and Applications, OOPSLA ’09, pages 761–762, New York, NY, USA. ACM.
Montagud, S., Abrahão, S., and Insfran, E. (2012). A systematic review of quality attributes and measures for software product lines. Software Quality Journal, 20(3-4):425– 486.
Montagud, S. and Abrahão, S. (2009). Gathering current knowledge about quality evaluation in software product lines. In Proceedings of the 13th International Software Product Line Conference, SPLC ’09, pages 91–100, Pittsburgh, PA, USA. Carnegie Mellon University.
Oliinyk, O., Petersen, K., Schoelzke, M., Becker, M., and Schneickert, S. (2015). Metrics for the evaluation of feature models in an industrial context: A case study at opel. In International Working Conference on Requirements Engineering: Foundation for Software Quality, pages 33–48. Springer.
Olumofin, F. G. and Misic, V. B. (2007). A holistic architecture assessment method for software product lines. Information and Software Technology, 49(4):309–323.
Pascual, G. G., Lopez-Herrejon, R. E., Pinto, M., Fuentes, L., and Egyed, A. (2015). Applying multiobjective evolutionary algorithms to dynamic software product lines for reconfiguring mobile applications. Journal of Systems and Software, 103:392–411.
Runeson, P. e Höst, M. (2009). Guidelines for conducting and reporting case study research in software engineering. Empirical software engineering, 14(2):131–164.
Saller, K., Lochau, M., and Reimund, I. (2013). Context-aware dspls: model-based runtime adaptation for resource-constrained systems. In Proceedings of the 17th International Software Product Line Conference co-located workshops, pages 106–113. ACM.
Thörn, C. (2010). On the quality of feature models.
Thüm, T., Apel, S., Kästner, C., Schaefer, I., and Saake, G. (2014). A classification and survey of analysis strategies for software product lines. ACM Computing Surveys (CSUR), 47(1):6.
Trinidad, P., Benavides, D., Durán, A., Ruiz-Cortés, A., and Toro, M. (2008). Automated error analysis for the agilization of feature modeling. Journal of Systems and Software, 81(6):883–896.
Wohlin, C., Runeson, P., Höst, M., Ohlsson, M. C., Regnell, B., and Wesslén, A. (2012). Experimentation in software engineering. Springer Science & Business Media.
Publicado
28/08/2017
Como Citar
BEZERRA, Carla I. M.; ANDRADE, Rossana M. C.; MONTEIRO, José Maria.
Medidas para Avaliação da Manutenibilidade do Modelo de Features de Linhas de Produto de Software Tradicionais e Dinâmicas. In: SIMPÓSIO BRASILEIRO DE QUALIDADE DE SOFTWARE (SBQS), 16. , 2017, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 385-399.
DOI: https://doi.org/10.5753/sbqs.2017.15119.
