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Manipulation of Training Sets for Improving Data Mining Coverage-Driven Verification

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Abstract

The constant pressure for making functional verification more agile has led to the conception of coverage driven verification (CDV) techniques. CDV has been implemented in verification testbenches using supervised learning techniques to model the relationship between coverage events and stimuli generation, providing a feedback between them. One commonly used technique is the classification- or decision-tree data mining, which has shown to be appropriate due to the easy modeling. Learning techniques are applied in two steps: training and application. Training is made on one or more sets of examples, which relate datasets to pre-determined classes. Precision of results by applying the predictive learning concept has shown to be sensitive to the size of the training set and the amount of imbalance of associated classes, this last meaning the number of datasets associated to each class is very different from each other. This work presents experiments on the manipulation of data mining training sets, by changing the size and reducing the imbalances, in order to check their influence on the CDV efficiency. To do that, a circuit example with a large input space and strong class imbalance was selected from the application domain of multimedia systems and another one, with a small input space that affects the coverage occurrences, was selected from the communication area.

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Notes

  1. Data mining refers to a variety of different learning techniques [17]. By following [5], in this article we will consider this term equivalent to the more specific classification-tree or decision-tree based data mining.

References

  1. Asaf S, Marcus E, Ziv A (2004) Defining coverage views to improve functional coverage analysis. In: Proceeedings of IEEE Design Automation Conference, pp 41–44

  2. Bartlett J, Kotrlik J, Higgins C (2001) Organizational research: determining appropriate sample size in survey research. Information Technology, Learning, and Performance Journal 19:43–50

    Google Scholar 

  3. Bergeron J (2003) Writing testbenches: functional verification of HDL models, 2nd edn. Kluwer, Boston

    Book  MATH  Google Scholar 

  4. Bramer M (2007) Principles of data mining. Springer, London

    MATH  Google Scholar 

  5. Braun M, Rosenstiel W, Schubert K (2003) Comparison of Bayesian networks and data mining for coverage directed verification. In: Proceedings of High Level Design Verification and Test Workshop, pp 91–95

  6. Chaudhuri S, Motwani R, Narasayya V (1998) Random sampling for histogram construction: how much is enough?". In: ACM International Conference on Management of Data, pp 436–447

  7. Chawla N, Bowyer K, Hall L, Kegelmeyer W (2002) SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16:321–357, AAAI Press

    MATH  Google Scholar 

  8. Corno F, Sánchez E, Sonza M, Squillero G (2004) Automatic test program generation: a case study. IEEE Design & Test of Computers 21(2):102–109

    Article  Google Scholar 

  9. Deng S, Kong Z, Bian J, Zhao Y (2009) Self-adjusting constrained random stimulus generation using splitting evenness evaluation and XOR constraints. In: Proceedings Asia and South Pacific design automation conference (ASP-DAC). Yokohama, Japan, pp 769–774

  10. Dobbin K, Simon R (2007) Sample size planning for developing classifiers using high dimensional DNA microarray data. Biostatistics 8:101–117

    Article  MATH  Google Scholar 

  11. Estabrooks A, Jo T, Japkowicz N (2004) A multiple resampling method for learning from imbalanced data sets. Computational Intelligence 20(1):18–36, Wiley

    Article  MathSciNet  Google Scholar 

  12. Fine S, Ziv A (2003) Coverage directed test generation for functional verification using bayesian networks. In Proceedings 40th design automation conference (DAC), Anaheim, CA, USA, pp 286–291

  13. Grinwald R, Harel E, Orgad M, Ur S, Ziv A (1998) User defined coverage a tool supported methodology for design verification. In: Proceedings of IEEE Design Automation Conference, pp 158–163

  14. Guzey O, Wang L, Levitt J, Foster H (2008) Functional test selection based on unsupervised support vector analysis. In: Proceedings 45th design automation conference (DAC), Anaheim, CA, USA, pp 262–267

  15. Hsueh H, Eder K (2006) Test directive generation for functional verification closure using inductive logic programming. In: Proceedings of High Level Design Validation and Test Workshop, pp 11–18

  16. Lachish O, Marcus E, Ur S, Ziv A (2002) Hole analysis for functional coverage data. In: Proceedings of Design Automation Conference, pp 807–812

  17. Maimon O, Rokach L (2005) Decomposition methodology for knowledge discovery and data mining: theory and applications. World Scientific, Singapore

    MATH  Google Scholar 

  18. Marquez C, Romero E, Strum M, Chau W (2011) A functional verification methodology based on parameter domains for efficient input stimuli generation and coverage modeling. Journal of Electronic Testing 27(4):485–503

    Article  Google Scholar 

  19. Piziali A (2004) Functional verification coverage measurement and analysis. Kluwer, Boston

    Google Scholar 

  20. Quinlan R (1992) C4.5: Programs for machine learning. Morgan Kaufmann, San Mateo, CA

  21. Romero E, Strum M, Chau WJ (2005) Comparing two testbench methods for hierarchical functional verification of a bluetooth baseband adaptor. In: Proceedings IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), Jersey City, pp 327–332. 16

  22. Romero E, Iguchi K, Strum M, Wang C (2007) Functional verification of communication systems based on modular coverage. In: 8th IEEE Latin-american test workshop (LATW), pp 37–42

  23. Romero EL, Strum M, Chau WJ (2012) Manipulation of training sets for improving data mining coverage-driven verification. In: 13th Latin american test workshop (LATW), pp 1–6

  24. Samarah A, Habibi A, Tahar S, Kharma N (2006) Automated coverage directed test generation using a cell-based genetic algorithm. In: Proceedings IEEE international high level design validation and test workshop (HLDVT), Monterey, CA, USA, pp 19–26

  25. Smith J, Bartley M, Fogarty T (1997) Microprocessor design verification by two-phase evolution of variable length tests. In: Proceedings of IEEE International Conference on Evolutionary Computing, pp 453–458

  26. Wagner I, Bertacco V, Austin T (2005) StressTest: an automatic approach to test generation via activity monitors. In: Proceedings of Design Automation Conference, Anaheim, pp 783–788

  27. WEKA 3 Data Mining Software in Java. WAIKATO University. http://www.cs.waikato.ac.nz/ml/weka/ Accessed 20 February 2013

  28. Wile B, Goss J, Roesner W (2005) Comprehensive functional verification. Morgan Kauffman, San Francisco

    Google Scholar 

Download references

Acknowledgments

This work was partially supported by the São Paulo Research Foundation FAPESP, and by the National Council of Technological and Scientific Development CNPq, both from Brazil.

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Authors and Affiliations

  1. School of Engineering, University of Sao Paulo, Av. Prof. Luciano Gualberto, trav.3 nº.158, 05508-900, Sao Paulo, Brazil

    Edgar Leonardo Romero, Marius Strum & Wang Jiang Chau

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  1. Edgar Leonardo Romero
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  2. Marius Strum
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  3. Wang Jiang Chau
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Correspondence to Wang Jiang Chau.

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Responsible Editor: F. L. Vargas

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Romero, E.L., Strum, M. & Chau, W.J. Manipulation of Training Sets for Improving Data Mining Coverage-Driven Verification. J Electron Test 29, 223–236 (2013). https://doi.org/10.1007/s10836-013-5372-1

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  • DOI: https://doi.org/10.1007/s10836-013-5372-1

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