By Eyal Bin, Avi Ziv, Shmuel Ur

This booklet constitutes the completely refereed post-proceedings of the second one foreign Haifa Verification convention, HVC 2006, held in Haifa, Israel, in October 2006.

The 15 revised complete papers provided including 2 invited lectures have been conscientiously reviewed and chosen from 33 submissions. The papers are equipped in 3 topical tracks on verification applied sciences and methodologies, software program checking out, and instruments for verification and software program testing.

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Additional info for Hardware and Software, Verification and Testing: Second International Haifa Verification Conference, HVC 2006, Haifa, Israel, October 23-26, 2006, Revised ... / Programming and Software Engineering)

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We use a template library to include such known functions. Matching these functions with given simulation data can be done easily. For example, we include all bit-wise logical operations such as bit-wise AND, OR, NOR, etc. as well as simple arithmetic functions like addition, subtraction, multiplication, and division. In addition, high-degree polynomials can be included to take care of special cases that are out of the scope of polynomial representation. As mentioned before, all functions in the template should be easily differentiable by checking with a small number of simulation samples.

Given a prefix α on input variables x1 , . . , xi , let fα denote the restricted function f (x1 , . . , xi = α). A learned OBDD can differ from the actual OBDD in two ways, as illustrated in Figure 4. Erroneous merge fα fα0 fα fα1 true Erroneous split xi fα xi+1 fα0 xi+2 fα00,α01 fα fα1 fα0, α1 fα0, α1 learned true learned Fig. 4. A learned OBDD may differ from the actual OBDD in two possible ways – (Erroneous merge). In the actual OBDD, fα0 and fα1 are two separated nodes. In the learned OBDD, they are merged into the same node (as a result, it disappears in the final OBDD structure).

Illustration of difference between Boolean and hybrid learning When the learner stops at the (n + 1)th layer, there may be more than two nodes in the layer. Hence, the result is an ordered multi-terminal decision diagram (OMDD). Each terminal node represents a unique function h where out = h(op1 , . . , opm ). After building the OMDD we can then use word-level learning methods to extract the functionality of each terminal individually. With some of the terminal nodes, word-level learning may fail to decide the functionality because of its complexity.

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