Introducing XCS to Coverage Directed test Generation

Charalambos Ioannides*, Geoff Barrett, Kerstin Eder

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

3 Citations (Scopus)

Abstract

Coverage Directed test Generation (CDG) is rife with challenges and problems, despite the relative successes of machine learning methodologies over the years in automating it. This paper introduces the use of the eXtended Classifier System (XCS) in simulation-based digital design verification. It argues for the use of this novel genetics-based machine learning technique to perform effective CDG by learning the full mapping between coverage results and test generator directives. Using the resulting production rules, efficient test suites can be constructed, and inference on the validity of the verification environment can be made. There is great potential in using XCS for design verification and this paper forms an initial attempt to highlight the associated advantages. The technique requires no domain knowledge to setup and satisfies important CDG requirements. Once matured, it is expected to be utilized seamlessly in any industrial level simulation-based verification process.

Original languageEnglish
Title of host publicationProceedings - IEEE International High-Level Design Validation and Test Workshop, HLDVT
Pages57-64
Number of pages8
DOIs
Publication statusPublished - 2011
Event16th IEEE International High Level Design Validation and Test Workshop, HLDVT'11 - Napa Valley, CA, United States
Duration: 10 Nov 201111 Nov 2011

Conference

Conference16th IEEE International High Level Design Validation and Test Workshop, HLDVT'11
CountryUnited States
CityNapa Valley, CA
Period10/11/1111/11/11

Keywords

  • Digital Simulation
  • Electronic Design Automation and Methodology
  • Learning Classifier Systems
  • Learning Systems
  • XCS

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