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Feedback-Based Coverage Directed Test Generation: An Industrial Evaluation

  • Charalambos Ioannides
  • Geoff Barrett
  • Kerstin Eder
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6504)

Abstract

Although there are quite a few approaches to Coverage Directed test Generation aided by Machine Learning which have been applied successfully to small and medium size digital designs, it is not clear how they would scale on more elaborate industrial-level designs. This paper evaluates one of these techniques, called MicroGP, on a fully fledged industrial design. The results indicate relative success evidenced by a good level of code coverage achieved with reasonably compact tests when compared to traditional test generation approaches. However, there is scope for improvement especially with respect to the diversity of the tests evolved.

Keywords

Microprocessor Verification Coverage Directed Test Generation Genetic Programming MicroGP 

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References

  1. 1.
    ITRS, International Technology Roadmap for Semiconductors, Design Chapter, 2008 edn. (2008) Google Scholar
  2. 2.
    Squillero, G.: MicroGP—An Evolutionary Assembly Program Generator. Genetic Programming and Evolvable Machines 6, 247–263 (2005)CrossRefGoogle Scholar
  3. 3.
    Piziali, A.: Functional verification coverage measurement and analysis. Springer, Berlin (2007)Google Scholar
  4. 4.
    Corno, F., Squillero, G., Reorda, M.S.: Code Generation for Functional Validation of Pipelined Microprocessors. In: Proceedings of the 8th IEEE European Test Workshop, p. 113. IEEE Computer Society, Los Alamitos (2003)Google Scholar
  5. 5.
    Corno, F., Cumani, G., Squillero, G.: Exploiting Auto-adaptive μGP for Highly Effective Test Programs Generation. In: Tyrrell, A.M., Haddow, P.C., Torresen, J. (eds.) ICES 2003. LNCS, vol. 2606, pp. 262–273. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Corno, F., Cumani, G., Reorda, M.S., Squillero, G.: Fully Automatic Test Program Generation for Microprocessor Cores. In: Proceedings of the conference on Design, Automation and Test in Europe, vol. 1, pp. 1006–1011. IEEE Computer Society, Los Alamitos (2003)Google Scholar
  7. 7.
    Corno, F., Sánchez, E., Reorda, M.S., Squillero, G.: Automatic Test Program Generation: A Case Study. IEEE Design & Test of Computers 21, 102–109 (2004)CrossRefGoogle Scholar
  8. 8.
    Corno, F., Cumani, G., Reorda, M.S., Squillero, G.: Automatic test program generation for pipelined processors. In: Proceedings of the 2003 ACM Symposium on Applied Computing, Melbourne, pp. 736–740. ACM, Florida (2003)CrossRefGoogle Scholar
  9. 9.
    Politechnico di Torino : Research: MicroGP (November 2007), http://www.cad.polito.it/research/microgp.html (accessed 2010-03-14)
  10. 10.
    Ur, S., Yadin, Y.: Micro architecture coverage directed generation of test programs. In: Proceedings of the 36th Annual ACM/IEEE Design Automation Conference, pp. 175–180. ACM, New Orleans (1999)Google Scholar
  11. 11.
    Bose, M., Shin, J., Rudnick, E.M., Dukes, T., Abadir, M.: A genetic approach to automatic bias generation for biased random instruction generation, pp. 442-448 (2001)Google Scholar
  12. 12.
    Tasiran, S., Fallah, F., Chinnery, D.G., Weber, S.J., Keutzer, K.: A functional validation technique: Biased-random simulation guided by observability-based coverage. Institute of Electrical and Electronics Engineers Inc., pp. 82-88 (2001)Google Scholar
  13. 13.
    Ashlock, D.: Evolutionary Computation for Modeling and Optimization. Springer, Heidelberg (2005)zbMATHGoogle Scholar
  14. 14.
    Koza, J.R.: Evolution and co-evolution of computer programs to control independently-acting agents. In: Proceedings of the first International Conference on Simulation of Adaptive Behavior on From Animals to Animats, pp. 366–375. MIT Press, Paris (1990)Google Scholar
  15. 15.
    Bernardi, P., Christou, K., Grosso, M., Michael, M.K., Sanchez, E., Reorda, M.S.: Exploiting MOEA to automatically generate test programs for path-delay faults in microprocessors, pp. 224–234. Springer, Heidelberg (2008)Google Scholar
  16. 16.
    Ravotto, D., Sanchez, E., Schillaci, M., Squillero, G.: An evolutionary methodology for test generation for peripheral cores via dynamic FSM extraction, pp. 214–223. Springer, Heidelberg (2008)Google Scholar
  17. 17.
    Ravotto, D., Sanchez, E., Schillaci, M., Squillero, G.: An evolutionary methodology for test generation for peripheral cores via dynamic FSM extraction, pp. 214–223. Springer, Heidelberg (2008)Google Scholar
  18. 18.
    Robinson, D.: Aspect-Oriented Programming with the e Verification Language: A Pragmatic Guide for Testbench Developers. Morgan Kaufmann, San Francisco (2007)zbMATHGoogle Scholar
  19. 19.
    Sanchez, E., Schillaci, M., Squillero, G.: Evolutionary Optimization: the μGP toolkit. Springer, Heidelberg (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Charalambos Ioannides
    • 1
    • 2
  • Geoff Barrett
    • 2
  • Kerstin Eder
    • 3
  1. 1.Industrial Doctorate Centre in SystemsUniversity of BristolBristolUK
  2. 2.Broadcom CorporationBroadcom BBE BUBristolUK
  3. 3.Department of Computer ScienceUniversity of BristolBristolUK

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