Advertisement

AVMf: An Open-Source Framework and Implementation of the Alternating Variable Method

  • Phil McMinn
  • Gregory M. Kapfhammer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9962)

Abstract

The Alternating Variable Method (AVM) has been shown to be a fast and effective local search technique for search-based software engineering. Recent improvements to the AVM have generalized the representations it can optimize and have provably reduced its running time. However, until now, there has been no general, publicly-available implementation of the AVM incorporating all of these developments. We introduce \(\mathrm{AVM}f\), an object-oriented Java framework that provides such an implementation. \(\mathrm{AVM}f\)  is available from http://avmframework.org  for configuration and use in a wide variety of projects.

Keywords

Memetic Algorithm Software Product Line Symbolic Execution Test Data Generation Objective Function Evaluation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Afshan, S., McMinn, P., Stevenson, M.: Evolving readable string test inputs using a natural language model to reduce human oracle cost. In: Proceedings of ICST (2013)Google Scholar
  2. 2.
    Arrieta, A., Wang, S., Sagardui, G., Etxeberria, L.: Test case prioritization of configurable cyber-physical systems with weight-based search algorithms. In: Proceedings of GECCO (2016)Google Scholar
  3. 3.
    Fraser, G., Arcuri, A., McMinn, P.: Test suite generation with memetic algorithms. In: Proceedings of GECCO (2013)Google Scholar
  4. 4.
    Fraser, G., Arcuri, A., McMinn, P.: A memetic algorithm for whole test suite generation. JSS 103, 311–327 (2015)Google Scholar
  5. 5.
    Fraser, G., Staats, M., McMinn, P., Arcuri, A., Padberg, F.: Does automated white-box test generation really help software testers? In: Proceedings of ISSTA (2013)Google Scholar
  6. 6.
    Fraser, G., Staats, M., McMinn, P., Arcuri, A., Padberg, F.: Does automated unit test generation really help software testers? a controlled empirical study. ACM TOSEM 24, 23 (2015)CrossRefGoogle Scholar
  7. 7.
    Harman, M., McMinn, P.: A theoretical and empirical analysis of evolutionary testing and hill climbing for structural test data generation. In: Proceedings of ISSTA (2007)Google Scholar
  8. 8.
    Harman, M., McMinn, P.: A theoretical and empirical study of search based testing: local, global and hybrid search. IEEE TSE 36, 226–247 (2010)Google Scholar
  9. 9.
    Kapfhammer, G.M., McMinn, P., Wright, C.J.: Search-based testing of relational schema integrity constraints across multiple database management systems. In: Proceedings of ICST (2013)Google Scholar
  10. 10.
    Kempka, J., McMinn, P., Sudholt, D.: A theoretical runtime and empirical analysis of different alternating variable searches for search-based testing. In: Proceedings of GECCO (2013)Google Scholar
  11. 11.
    Kempka, J., McMinn, P., Sudholt, D.: Design and analysis of different alternating variable searches for search-based software testing. TCS 605, 1–20 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Korel, B.: Automated software test data generation. IEEE TSE (1990)Google Scholar
  13. 13.
    Kukunas, J., Cupper, R.D., Kapfhammer, G.M.: A genetic algorithm to improve Linux kernel performance on resource-constrained devices. In: Proc. GECCO (2010)Google Scholar
  14. 14.
    Lakhotia, K., Harman, M., Gross, H.: AUSTIN: A tool for search based software testing for the C language and its evaluation on deployed automotive systems. In: SSBSE (2010)Google Scholar
  15. 15.
    Lakhotia, K., Harman, M., Gross, H.: AUSTIN: An open source tool for search based software testing of C programs. IST 55, 112–125 (2013)Google Scholar
  16. 16.
    Lakhotia, K., Tillmann, N., Harman, M., de Halleux, J.: FloPSy - search-based floating point constraint solving for symbolic execution. In: Petrenko, A., Simão, A., Maldonado, J.C. (eds.) ICTSS 2010. LNCS, vol. 6435, pp. 142–157. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    McMinn, P.: IGUANA: Input generation using automated novel algorithms. A plug and play research tool. Technical Report CS-07-14, Dept. Computer Science, University of Sheffield, UK (2007)Google Scholar
  18. 18.
    McMinn, P., Wright, C.J., Kapfhammer, G.M.: The effectiveness of test coverage criteria for relational database schema integrity constraints. ACM TOSEM 25, 8:1–8:49 (2015)CrossRefGoogle Scholar
  19. 19.
    Pradhan, D., Wang, S., Ali, S., Yue, T.: Search-based cost-effective test case selection for manual execution within time budget: an empirical study. In: Proceedings of GECCO (2016)Google Scholar
  20. 20.
    Qiu, X., Ali, S., Yue, T., Zhang, L.: Reliability-redundancy-location allocation with maximum reliability and minimum cost using search techniques. IST (2016, to appear)Google Scholar
  21. 21.
    Yue, T., Ali, S.: Applying search algorithms for optimizing stakeholders familiarity and balancing workload in requirements assignment. In: Proceedings of GECCO (2014)Google Scholar
  22. 22.
    Yue, T., Ali, S., Lu, H., Nie, K.: Search-based decision ordering to facilitate product line engineering of cyber-physical system. In: Proceedings of MODELSWARD (2016)Google Scholar
  23. 23.
    Zhong, H., Su, Z.: An empirical study on real bug fixes. In: Proceedings of ICSE (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.University of SheffieldSheffieldUK
  2. 2.Allegheny CollegeMeadvilleUSA

Personalised recommendations