Yehuda Naveh

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We present X-Gen, a model-based test-case generator designed for systems and systems on a chip (SoCs). XGen provides a framework and a set of building blocks for system-level test-case generation. At the core of this framework lies a system model, which consists of component types, their configuration, and the interactions between them. Building blocks(More)
Matching highly skilled people to available positions is a high-stakes task that requires careful consideration by experienced resource managers. A wrong decision may result in significant loss of value due to understaffing, underqualification or overqualification of assigned personnel, and high turnover of poorly matched workers. While the importance of(More)
We report on random stimuli generation for hardware verification in IBM as a major application of various artificial intelligence technologies, including knowledge representation, expert systems, and constraint satisfaction. The application has been developed for almost a decade, with huge payoffs. Research and development around this application is still(More)
Today many companies face the challenge of matching highly-skilled professionals to high-end positions in large organizations and human deployment agencies. Unlike traditional Workforce Management problems such as shift scheduling, highly-skilled employees are professionally distinguishable from each other and hence non-interchangeable. Our work(More)
Functional verification of systems is aimed at validating the integration of previously verified components. It deals with complex designs, and invariably suffers from scarce resources. We present a set of methods, collectively known as testing knowledge, aimed at increasing the quality of automatically generated system-level test-cases. Testing knowledge(More)
A new generic method for solving constraint satisfaction problems is presented. The method is based on stochastic search which is non-local in the sense that at any point in the search, the next state sampled may be arbitrarily far from the current state. The solver relies heavily on knowledge of the high-level characteristics of the topography defining the(More)
The initial state of a design under verification has a major impact on the ability of stimuli generators to successfully generate the requested stimuli. For complexity reasons, most stimuli generators use sequential solutions without planning ahead. Therefore, in many cases, they fail to produce a consistent stimuli due to an inadequate selection of the(More)