Automatic Scalable System for the Coverage-Directed Generation (CDG) Problem
Raviv Gal1,a, Eldad Haber2, Wesam Ibraheem1,b, Brian Irwin2,b, Ziv Nevo1,c and Avi Ziv1,d
1Hybrid Cloud Quality Technologies Department, IBM Research - Haifa, Israel
aravivg@il.ibm.com
bwesam@il.ibm.com
cnevo@il.ibm.com
daziv@il.ibm.com
2Department of Earth and Ocean Science, The University of British Columbia, Vancouver, BC, Canada
ahaber@eoas.ubc.ca
bbirwin@eoas.ubc.ca
ABSTRACT
We present AS-CDG, a novel automatic scalable system for data-driven coverage-directed generation. The goal of AS-CDG is to find the test templates that maximize the probability of hitting uncovered events. The system contains two phases, one for a coarse-grained search that finds relevant parameters and the other for a fine-grained search for the settings of these parameters. To overcome the lack of evidence in the search, we replace the real target with an approximated target induced by neighboring events, for which we have evidence. Usage results on real-life units of high-end processors illustrate the ability of the proposed system to automatically find the desired test-templates and hit the previously uncovered target events.
