Application of Memetic Algorithms in the Search-based Product Line Architecture Design: An Exploratory Study

João Choma Neto, Thelma E. Colanzi, Aline M. M. Miotto Amaral

2017

Abstract

Basic design principles, feature modularization, and SPL extensibility of Product Line Architecture (PLA) design have been optimized by multi-objective genetic algorithms. Until now, memetic algorithms have not been used for PLA design optimization. Considering that memetic algorithms (MA) have achieved better quality solutions than the solutions obtained by genetic algorithms (GA) and that previous study involving the application of design patterns to PLA design optimization returned promising results, bringing the motivation in investigating the use of MA and the Design Pattern Search Operator as local search to the referred context. This work presents an exploratory study aimed to characterize the application of using MA in PLA design optimization. When compared with a GA approach, the results show thatMAare promising, since the obtained solutions are slightly better than solutions found by the GA. A pattern application rate was identified in about 30 % of the solutions obtained by MA. However, the qualitative analysis showed that the existing global search operators need to be refactored for the joint use with the MA approach.

References

  1. Chawla, P., Chana, I., and Rana, A. (2015). A novel strategy for automatic test data generation using soft computing technique. Frontiers of Comp.Science, 9(3):346- 363.
  2. Colanzi, T. E., Vergilio, S. R., Gimenes, I. M. S., and Oizumi, W. N. (2014). A search-based approach for software product line design. In Proc. of SPLC 2014.
  3. Contieri Jr, A. C., Correia, G. G., Colanzi, T. E., Gimenes, I. M., Oliveira Jr, E. A., Ferrari, S., Masiero, P. C., and Garcia, A. F. (2011). Extending uml components to develop software product-line architectures: lessons learned. In European Conference on Software Architecture, pages 130-138. Springer.
  4. Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput., 6(2):182-197.
  5. Donegan, P. M. and Masiero, P. C. (2007). Design issues in a component-based software product line. In SBCARS, pages 3-16.
  6. Féderle, E. L., Ferreira, T. N., Colanzi, T. E., and Vergilio, S. R. (2015). OPLA-Tool: A support tool for searchbased product line architecture design. In Proc. of the 19th International Conference on Software Product Line, SPLC 7815, pages 370-373.
  7. Ferrucci, F., Harman, M., Ren, J., and Sarro, F. (2013). Not going to take this anymore: Multi-objective overtime planning for Software Engineering projects. Proceedings - International Conference on Software Engineering, pages 462-471.
  8. Fraser, G., Arcuri, A., and McMinn, P. (2015). A memetic algorithm for whole test suite generation. Journal of Systems and Software, 103:311-327.
  9. Gomaa, H. (2011). Software modeling and design: UML, use cases, patterns, and software architectures. Cambridge University Press.
  10. Guizzo, G., Colanzi, T., and Vergilio, S. (2014). A patterndriven mutation operator for search-based product line architecture design. In Proc. of SSBSE, pages 77-91.
  11. Harman, M. and McMinn, P. (2010). A theoretical and empirical study of search-based testing: Local, global, and hybrid search. IEEE Trans. Soft. Eng., 36(2):226- 247.
  12. Jeya Mala, D., Sabari Nathan, K., and Balamurugan, S. (2013). Critical components testing using hybrid genetic algorithm. SIGSOFT Softw.Eng.Notes, 38(5):1- 13.
  13. Nunes, C., Kulesza, U., Sant'Anna, C., Nunes, I., Garcia, A., and Lucena, C. (2009). Assessment of the design modularity and stability of multi-agent system product lines. Journal of Universal Computer Science, 15(11):2254-2283.
  14. Ochoa, G., Verel, S., and Tomassini, M. (2010). Firstimprovement vs. best-improvement local optima networks of nk landscapes. In International Conference on Parallel Problem Solving from Nature, pages 104- 113. Springer.
  15. OliveiraJr, E., Gimenes, I. M., Maldonado, J. C., Masiero, P. C., and Barroca, L. (2013). Systematic evaluation of software product line architectures. Journal of Universal Computer Science, 19:25-52.
  16. Radziukyniene?, I. and Z?ilinskas, A. (2008). Evolutionary methods for multi-objective portfolio optimization. In Proceedings of the World Congress on Engineering, volume 2.
  17. Russell, S. J. and Norvig, P. (2003). Artificial Intelligence: A Modern Approach. Pearson Education, 2 edition.
  18. SEI (2016). AGM.
  19. Smith, J. and Simons, C. L. (2013). A comparison of two memetic algorithms for software class modelling. In Proc. of GECCO, pages 1485-1492, New York, USA. ACM.
  20. van der Linden, F. and Rommes, E. (2007). Software Product Lines in Action - The Best Industrial Practice in Product Line Engineering. Springer.
  21. Van Veldhuizen, D. A. (1999). Multiobjective evolutionary algorithms: classifications, analyses, and new innovations. Technical report, DTIC Document.
  22. Van Veldhuizen, D. A. and Lamont, G. B. (1998). Multiobjective evolutionary algorithm research: A history and analysis. Technical report, Citeseer.
  23. Wust, J. (2016). SDMetrics. http://www.sdmetrics.com/. Accessed on 05/12/2016.
  24. Yoo, S. and Harman, M. (2007). Pareto efficient multiobjective test case selection. In Proceedings of the 2007 international symposium on Software testing and analysis, pages 140-150. ACM.
  25. Zeleny, M. and Cochrane, J. L. (1973). Multiple criteria decision making. University of South Carolina Press.
  26. Zitzler, E., Laumanns, M., Thiele, L., et al. (2001). Spea2: Improving the strength pareto evolutionary algorithm. In Eurogen, volume 3242, pages 95-100.
  27. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M., and Da Fonseca, V. G. (2003). Performance assessment of multiobjective optimizers: an analysis and review. IEEE transactions on evolutionary computation, 7(2):117-132.
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in Harvard Style

Choma Neto J., Colanzi T. and M. M. Miotto Amaral A. (2017). Application of Memetic Algorithms in the Search-based Product Line Architecture Design: An Exploratory Study . In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-248-6, pages 178-189. DOI: 10.5220/0006363201780189


in Bibtex Style

@conference{iceis17,
author={João Choma Neto and Thelma E. Colanzi and Aline M. M. Miotto Amaral},
title={Application of Memetic Algorithms in the Search-based Product Line Architecture Design: An Exploratory Study},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2017},
pages={178-189},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006363201780189},
isbn={978-989-758-248-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Application of Memetic Algorithms in the Search-based Product Line Architecture Design: An Exploratory Study
SN - 978-989-758-248-6
AU - Choma Neto J.
AU - Colanzi T.
AU - M. M. Miotto Amaral A.
PY - 2017
SP - 178
EP - 189
DO - 10.5220/0006363201780189