Yehuda Naveh

Learn More
We present X-Gen, a model-based test-case generator designed for systems and systems on a chip (SoCs). X-Gen 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)
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)
—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)
This work presents methods for processing a constraint satisfaction problem (CSP) formulated by an expression-based language, before the CSP is presented to a stochastic local search solver. The architecture we use to implement the methods allows the extension of the expression language by user-defined operators, while still benefiting from the processing(More)