Download An Artificial Intelligence Approach to Test Generation by Narinder Singh PDF

By Narinder Singh

I am indebted to my thesis consultant, Michael Genesereth, for his information, proposal, and aid which has made this learn attainable. As a instructor and a sounding board for brand new principles, Mike used to be tremendous precious in mentioning Haws, and suggesting new instructions to discover. i might additionally prefer to thank Harold Brown for introducing me to the applying of man-made intelligence to reasoning approximately designs, and his many beneficial reviews as a reader of this thesis. major contribu­ tions by way of the opposite contributors of my examining committee, Mark Horowitz, and Allen Peterson have vastly better the content material and association of this thesis by way of forcing me to speak my rules extra sincerely. i'm tremendous thankful to the opposite individuals of the good judgment staff on the Heuristic Programming undertaking for being a sounding board for my principles, and offering precious reviews. particularly, i want 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 way of Schlumberger Palo Alto learn (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 offering an outstanding atmosphere for acting this research.

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However, the computational savings in using the more appropriate formulation cannot come at the expense of correctness. The behavior of each component in the design must be correct, and the behavior of a composite component must be a correct abstraction of the composition of the behaviors of its subparts. However, the two behaviors need not be equivalent, since in defining the behavior of more abstract components we will usually want to ignore the details that are irrelevant at that level. There is a tension between the two competing goals of correctness and ignoring detail.

Refining a design is the inverse of abstraction, and involves creating a collection of objects that refine an existing object in the original design. For example, we can refine a design by defining the substructure of an existing primitive object. Due to the duality of abstraction and refinement, the discussion in the following subsection for abstraction is equally applicable for refinement. 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.

Before we define how designs can be reformulated, we present a precise definition of a device which is a physical entity, and a design, which is a specification of this device. We will next examine the different types of design reformulation operations, and demonstrate the utility of performing these reformulations by examining their impact on the size of the search space, and the size of the design. We will show that the different reformulation operations can reduce both the depth and branching factor of the search space, and also reduce the size of the design.

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