Skip to main content

On the Application of the Multi-Evolutionary and Coupling-Based Approach with Different Aspect-Class Integration Testing Strategies

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 8084))

Abstract

During the integration test of aspect-oriented software, it is necessary to determine an aspect-class integration and test order, associated to a minimal possible stubbing cost. To determine such optimal orders an approach based on multi-objective evolutionary algorithms was proposed. It generates a set of good orders with a balanced compromise among different measures and factors that may influence the stubbing process. However, in the literature there are different strategies proposed to aspect-class integration. For instance, the classes and aspects can be integrated in a combined strategy, or in an incremental way. The few works evaluating such strategies do not consider the multi-objective and coupling based approach. Given the importance of such approach to reduce testing efforts, in this work, we conduct an empirical study and present results from the application of the multi-objective approach with both mentioned strategies. The approach is implemented with four coupling measures and three evolutionary algorithms that are also evaluated: NSGA-II, SPEA2 and PAES. We observe that different strategies imply in different ways to explore the search space. Moreover, other results related to the practical use of both strategies are presented.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alexander, R.T., Bieman, J.M., Andrews, A.A.: Towards the Systematic Testing of Aspect-Oriented Programs. Tech. rep., Colorado State University (2004)

    Google Scholar 

  2. Arcuri, A., Fraser, G.: On parameter tuning in search based software engineering. In: Cohen, M.B., Ó Cinnéide, M. (eds.) SSBSE 2011. LNCS, vol. 6956, pp. 33–47. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  3. Assunção, W.K.G., Colanzi, T.E., Pozo, A., Vergilio, S.R.: Establishing integration test orders of classes with several coupling measures. In: 13th Genetic and Evolutionary Computation Conference (GECCO), pp. 1867–1874 (2011)

    Google Scholar 

  4. Assunção, W., Colanzi, T., Vergilio, S., Pozo, A.: Generating integration test orders for aspect-oriented software with multi-objective algorithms. RITA-Revista de Informática Teórica e Aplicada 20(2), 301–327 (2013)

    Google Scholar 

  5. Briand, L.C., Labiche, Y.: An investigation of graph-based class integration test order strategies. IEEE Trans. on Software Engineering 29(7), 594–607 (2003)

    Article  Google Scholar 

  6. Ceccato, M., Tonella, P., Ricca, F.: Is AOP code easier or harder to test than OOP code. In: Workshop on Testing Aspect-Oriented Program, WTAOP (2005)

    Google Scholar 

  7. Cochrane, J., Zeleny, M.: Multiple Criteria Decision Making. University of South Carolina Press, Columbia (1973)

    Google Scholar 

  8. Colanzi, T.E., Assunção, W.K.G., Vergilio, S.R., Pozo, A.: Integration test of classes and aspects with a multi-evolutionary and coupling-based approach. In: Cohen, M.B., Ó Cinnéide, M. (eds.) SSBSE 2011. LNCS, vol. 6956, pp. 188–203. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  9. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)

    Article  Google Scholar 

  10. Durillo, J., Nebro, A., Alba, E.: The jMetal framework for multi-objective optimization: Design and architecture. In: IEEE Congress on Evolutionary Computation (CEC), Barcelona, Spain, pp. 4138–4325 (July 2010)

    Google Scholar 

  11. Galvan, R., Pozo, A., Vergilio, S.: Establishing Integration Test Orders for Aspect-Oriented Programs with an Evolutionary Strategy. In: Latinamerican Workshop on Aspect Oriented Software (2010)

    Google Scholar 

  12. Gárcia, S., Molina, D., Lozano, M., Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization. Journal of Heuristics 15(6), 617–644 (2009)

    Article  MATH  Google Scholar 

  13. Harman, M., Islam, F., Xie, T., Wappler, S.: Automated test data generation for aspect-oriented programs. In: 8th ACM International Conference on Aspect-Oriented Software Development (AOSD), pp. 185–196. ACM (2009)

    Google Scholar 

  14. Knowles, J.D., Corne, D.W.: Approximating the nondominated front using the Pareto archived evolution strategy. Evol. Comput. 8, 149–172 (2000)

    Article  Google Scholar 

  15. Lakhotia, K., Harman, M., McMinn, P.: A multi-objective approach to search-based test data generation. In: Annual Conference on Genetic and Evolutionary Computation (GECCO), pp. 1098–1105 (2007)

    Google Scholar 

  16. Lemos, O.A.L., Vincenzi, A.M.R., Maldonado, J.C., Masiero, P.C.: Control and data flow structural testing criteria for aspect-oriented programs. The Journal of Systems and Software 80, 862–882 (2007)

    Article  Google Scholar 

  17. Massicotte, P., Badri, L., Badri, M.: Aspects-classes integration testing strategy: An incremental approach. In: Guelfi, N., Savidis, A. (eds.) RISE 2005. LNCS, vol. 3943, pp. 158–173. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  18. Melton, H., Tempero, E.: An empirical study of cycles among classes in Java. Empirical Software Engineering 12, 389–415 (2007)

    Article  Google Scholar 

  19. Pareto, V.: Manuel D’Economie Politique. Ams Press, Paris (1927)

    Google Scholar 

  20. Ré, R., Lemos, O.A.L., Masiero, P.C.: Minimizing stub creation during integration test of aspect-oriented programs. In: 3rd Workshop on Testing Aspect-Oriented Programs, Vancouver, British Columbia, Canada, pp. 1–6 (March 2007)

    Google Scholar 

  21. Ré, R., Masiero, P.C.: Integration testing of aspect-oriented programs: a characterization study to evaluate how to minimize the number of stubs. In: Brazilian Symposium on Software Engineering (SBES), pp. 411–426 (October 2007)

    Google Scholar 

  22. Vergilio, S.R., Pozo, A., Árias, J.C., Cabral, R.V., Nobre, T.: Multi-objective optimization algorithms applied to the class integration and test order problem. Software Tools for Technology Transfer 14, 461–475 (2012)

    Article  Google Scholar 

  23. Wang, Z., Li, B., Wang, L., Li, Q.: A brief survey on automatic integration test order generation. In: Software Engineering and Knowledge Engineering Conference (SEKE), pp. 254–257 (2011)

    Google Scholar 

  24. Yoo, S., Harman, M.: Pareto Efficient Multi-Objective Test Case Selection. In: International Symposium on Software Testing and Analysis, pp. 140–150 (2007)

    Google Scholar 

  25. Zhao, J.: Data-flow based unit testing of aspect-oriented programs. In: 27th Conference on Computer Software and Applications, Washington, DC (2003)

    Google Scholar 

  26. Zhou, Y., Ziv, H., Richardson, D.J.: Towards a practical approach to test aspect-oriented software. In: Workshop on Testing Component-based Systems (TECOS), vol. 58, pp. 1–16 (2004)

    Google Scholar 

  27. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Tech. Rep. 103, Swiss Federal Institute of Technology (ETH) Zurich, CH-8092, Zurich, Switzerland (2001)

    Google Scholar 

  28. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., da Fonseca, V.G.: Performance assessment of multiobjective optimizers: An analysis and review. IEEE Transactions on Evolutionary Computation 7, 117–132 (2003)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Assunção, W.K.G., Colanzi, T.E., Vergilio, S.R., Pozo, A. (2013). On the Application of the Multi-Evolutionary and Coupling-Based Approach with Different Aspect-Class Integration Testing Strategies. In: Ruhe, G., Zhang, Y. (eds) Search Based Software Engineering. SSBSE 2013. Lecture Notes in Computer Science, vol 8084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39742-4_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-39742-4_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39741-7

  • Online ISBN: 978-3-642-39742-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics