Abstract: Systems and methods for creating learning-based personalized user interfaces for software applications are described. Exemplary embodiments provide for collecting usage data and applying machine learning techniques to identify and prioritize certain commands and options in the personalized user interface. The usage data can include Usage patterns, usage sequences, and the usage of certain commands and options in connection with, or following, certain other commands and options may also be identified, and the personalization-based prioritization can include, for example, the contents, position, and quantities of the commands and options within the interface.

Abstract: An integrated circuit test method provides an interactive shell environment having analysis modules organized as a directory such that for a given session a user can access any of the analysis modules. This invention describes a virtual directory structure for navigating through the entire test data starting from design, test configuration, ATPG patterns, failure information and callout information. This structure also allows the creation of a scripting environment for the user to select a specific configuration and process the information. User can achieve all of this in a single session as opposed to working on every test configuration in an independent session.

Abstract: Systems and methods disclosed herein provide for automatically diagnosing mis-compares detected during simulation of Automatic Test Pattern Generation (“ATPG”) generated test patterns. Embodiments of the systems and methods provide for determining the origin of a mis-compare based on an analysis of the generated test patterns with a structural simulator and a behavioral simulator.