ACCESSIBILITY VERIFICATION TESTING

    公开(公告)号:US20250068542A1

    公开(公告)日:2025-02-27

    申请号:US18452620

    申请日:2023-08-21

    Abstract: A computer-implemented method includes identifying a webpage comprising a set of user interface (UI) elements, analyzing the set of user UI elements to identify a set of interactable elements, classifying the elements of the set interactable elements as either focusable or not focusable, extracting features from source code corresponding to interactable elements of the set of interactable elements classified as focusable, and building an accessibility issue detection model using the extracted features from source code corresponding to focusable interactable elements as training data. The method may further include extracting features from source code corresponding to interactable elements classified as not focusable and updating the accessibility issue detection model using the extracted features from source code corresponding to interactable elements which are not focusable as training data. The method may further include storing one or more passed results and one or more failed results in a results database.

    Security architecture system, security management method, and computing device

    公开(公告)号:US12235996B2

    公开(公告)日:2025-02-25

    申请号:US17993428

    申请日:2022-11-23

    Abstract: A security architecture system includes a plurality of subsystems. The plurality of subsystems include a secure element subsystem. A first subsystem of the plurality of subsystems includes a trusted computing platform that has a trusted platform control module. The first subsystem is configured to, for a running object in one or more subsystems other than the first subsystem in the plurality of subsystems, use the trusted platform control module to perform security measurement on the running object based on a measurement strategy and a measurement benchmark value to obtain a measurement result. The measurement result is used to control a running state of the running object in one or more subsystems other than the first subsystem in the plurality of subsystems.

    BINARY INSTRUMENTATION FOR COMPARATIVE PERFORMANCE AND EFFICIENCY ANALYSIS IN EMULATED ENVIRONMENTS

    公开(公告)号:US20250061045A1

    公开(公告)日:2025-02-20

    申请号:US18779505

    申请日:2024-07-22

    Applicant: Google LLC

    Abstract: Aspects of the technology provide a software testing framework that can significantly reduce hardware resources needed to validate code modules. This may include employing a hardware emulator capable of instrumenting binaries to produce a trace of operations performed by a given program. The trace of operations performed by the given program may be mapped to representative profiles of operations benchmarked on a hardware system corresponding to the hardware emulated by the hardware emulator. The representative profile may contain sets of representative operations previously performed on the hardware. The mapping may allow for estimates on performance metrics of the given program (e.g., efficacy and/or speed) when run on the hardware. Such estimates may allow for the identification of operations that cause the given program to run inefficiently or slowly on the hardware.

    GENERATING IDEATION SCENARIOS BASED ON INTENTS ASSOCIATED WITH REQUIREMENTS

    公开(公告)号:US20250060942A1

    公开(公告)日:2025-02-20

    申请号:US18233958

    申请日:2023-08-15

    Abstract: A computer-implemented method, system, and computer program product for generating ideation scenarios. A requirement to be used in ideation is received. The intent associated with the received requirement is then identified. An intent refers to the actions to be performed to accomplish the purpose or objective of the requirement. Each intent is associated with a requirement as well as one or more inputs and a final output. A pre-requirement and a post-requirement to the received requirement are then identified. Upon identifying such requirements, the intents associated with such requirements are identified. Upon identifying the intents, such as for the received requirement and the pre- and post-requirements, such intents are joined to generate ideation scenarios in terms of actions. An ideation scenario refers to the idea or solution based on a requirement, where the ideation scenario includes the intents along with the associated requirements, inputs and final outputs.

    APPLICATION FUNCTIONALITY TESTING, RESILIENCY TESTING, CHAOS TESTING, AND PERFORMANCE TESTING USING MACHINE LEARNING

    公开(公告)号:US20250053501A1

    公开(公告)日:2025-02-13

    申请号:US18932467

    申请日:2024-10-30

    Abstract: A network system to use machine learning systems to create chaos testing scenarios on cloud-based applications. The system uses inputs from applications that are implemented on user computing devices to allow users to interface with a network or other system. The system creates a model of the application based on input data received from a network of applications, the model representing a structure, method, and dependencies of the application. The system identifies points of failure of the application and generates one or more chaos testing simulation scenarios that target the identified points of failure. The system performs the chaos testing based on the received simulation scenarios and logs the results of the testing. The system generates recommendations to revise code of the application based on the outcome of the chaos testing. A large language model may be used to provide documentation and analysis of the chaos testing.

    Specification to Test using Generative Artificial Intelligence

    公开(公告)号:US20250053500A1

    公开(公告)日:2025-02-13

    申请号:US18796978

    申请日:2024-08-07

    Abstract: Apparatuses, systems, and methods for generative Artificial Intelligence (AI) assisted test process development based on an initial input of a specification of a device under test (DUT). The specification of the DUT may be inputted into the Generative AI model. The Generative AI model may summarize the specification, request further input via an interaction with an end user to finalize a description of the DUT, and generate/create test assets, such as code, documentation, tables, diagrams, and so forth. The Generative AI model may collaborate with the end user to refine outputs from the test assets. The refined test assets may be sent to software applications that can use/run/deploy various test assets. Additionally, the generative AI may access local test hardware and enumerate test hardware on other systems via network/serial communications to create a test system that fits the identified hardware.

    Systems and methods for testing virtual functions of a device under test

    公开(公告)号:US12222844B2

    公开(公告)日:2025-02-11

    申请号:US18113934

    申请日:2023-02-24

    Abstract: Embodiments of the present invention can provide an extended NVMe driver that supports exercising virtual functions (and related physical functions) of a DUT without using a VM or hypervisor. In this way, the amount of memory and processing resources used for testing NVMe SSDs can be significantly reduced, and a large number of DUTs (e.g., up to 16 DUTs) can be tested in parallel independently. In other words, each DUT is tested in isolation, as if is the only device being tested, and there are no race conditions or competition for resources between workloads during testing.

    TESTING AGENT FOR APPLICATION DEPENDENCY DISCOVERY, REPORTING, AND MANAGEMENT TOOL

    公开(公告)号:US20250045193A1

    公开(公告)日:2025-02-06

    申请号:US18798411

    申请日:2024-08-08

    Abstract: Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls. Such tests may be used to train the machine learning model.

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