An Artificial Intelligence Approach to Test Generation by Narinder Singh

By Narinder Singh

I am indebted to my thesis consultant, Michael Genesereth, for his assistance, proposal, and help which has made this study attainable. As a instructor and a sounding board for brand spanking new rules, Mike used to be super important in declaring Haws, and suggesting new instructions to discover. i'd additionally prefer to thank Harold Brown for introducing me to the applying of man-made intelligence to reasoning approximately designs, and his many worthwhile reviews as a reader of this thesis. major contribu­ tions through the opposite contributors of my studying committee, Mark Horowitz, and Allen Peterson have drastically superior the content material and association of this thesis by means of forcing me to speak my principles extra essentially. i'm super thankful to the opposite individuals of the common sense team on the Heuristic Programming undertaking for being a sounding board for my principles, and delivering important reviews. particularly, i need to thank Matt Ginsberg, Vineet Singh, Devika Subramanian, Richard Trietel, Dave Smith, Jock Mackinlay, and Glenn Kramer for his or her pointed criticisms. This learn was once supported by means of Schlumberger Palo Alto examine (previously Fairchild Laboratory for synthetic Intelligence). i'm thankful to Peter Hart, the previous head of the AI lab, and his successor Marty Tenenbaum for supplying a superb surroundings for appearing this research.

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In reasoning with the higher level design formulation we are replacing the entire search space at the lower level with a single node and its children. The advantages of reasoning with the reformulated design are similar to those described in the previous subsection for structural abstraction, and the analysis performed there is equally applicable for spatial abstraction. S Temporal Abatraction Temporal abstraction corresponds to the cases where the values at the abstract level are related to the values at the lower level with some delay.

Different choices in the search space can converge at a common set of fanout nodes to define identical subgoals. Fanouts are used to share hardware across different functions to minimize the area, and the power consumption of the device. Since fanouts are common in digital designs, redundant subgoals can be expected to occur frequently in the search space. The nodes in the search space also include extraneous conjuncts in their subgoals. For example, in controlling the carry output to 1 the last conjunct b in the solution cab is extraneous.

For a system of non-linear equations, however, constraint propagation can be as expensive as search due to the cost of solving the constraints. In this case it can be more advantageous to employ search if there are good heuristics to guide the selection of promising alternatives at each node in the search space (described in Chapter 4). 2 Repartitioning Designs Repartitioning a design involves choosing a different set of objects for a design such that the primitive objects in the new and old design are the same.

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