25296results about "Error detection/correction" patented technology

The invention provides a multi-source software supply chain intelligent analysis method and system. The method comprises the following steps: analyzing software component identification information submitted by a user, extracting attribute characteristics, positioning node paths among components, and generating a weighted dependency chain list containing direct and indirect dependency chains; a Bayesian network model is combined with a predefined risk index system, the risk score of each dependency relationship chain is calculated, potential supply chain threat points are identified through an anomaly detection algorithm, and a risk report is generated; the report covers the overall risk score and the threat point of the key link or component; based on the report, drawing a comprehensive risk map by using a multi-dimensional visualization algorithm, and displaying component groups with similar risk modes; and a genetic algorithm and a simulated annealing algorithm are fused to explore a high-risk area, an adjustment suggestion is proposed, and a risk mitigation strategy is generated to reduce the overall risk score. According to the technical scheme provided by the embodiment of the invention, the security and integrity of the multi-source software supply chain are improved.

The invention discloses an intelligent programming auxiliary method and system based on a multi-modal AI language model, and belongs to the technical field of programming auxiliary tools. The method comprises the following steps: a multi-modal input processing stage; a dynamic context modeling stage; a hierarchical semantic analysis stage; in the code generation stage, codes are generated in two stages by adopting a Codex-Plus large model; the reinforcement learning driven code optimization stage is used for carrying out multi-objective optimization and reward function design on the codes generated in the code generation stage; a multi-dimensional feedback stage; an interaction and visualization stage; code semantic deep analysis, dynamic context sensing, multi-target optimization generation and real-time interactive feedback are realized by fusing code texts, natural language description, developer behavior data and a domain knowledge graph, and programming efficiency and code quality can be remarkably improved.

The invention relates to the technical field of computers, provides an automatic test script dynamic generation method and system based on multi-modal AI recognition, and is used for remarkably improving the coverage dimension and execution robustness of test cases while reducing the manual maintenance cost. The method comprises the following steps: acquiring target test interface data, extracting dynamic interaction characteristics in the test interface data, calling a trained target detection network to carry out element positioning processing on the test interface data, generating an interface element coordinate set, and generating a target test interface based on the interface element coordinate set. Calling a character recognition network to carry out multi-modal feature analysis on the interface element distribution information, and generating a feature fusion result containing character semantic features and icon classification features; the feature fusion result is input into a script generation model for dynamic path planning processing, automatic test script elements are output, and the automatic test script elements comprise logic control instructions generated based on the element operation sequence and coordinate self-correction parameters.

The embodiment of the invention provides a cross-chip platform compiling tool chain method which comprises the following steps: scanning a target platform architecture specification file to extract instruction set features, constructing a platform difference matrix based on the instruction set features to record instruction formats and system call differences of different chip platforms, and normalizing a multi-language syntax tree into a unified intermediate representation; constructing a cross-platform compiler to convert the intermediate representation into a target code; designing a unified intermediate language based on a platform difference matrix to support multi-language compilation; creating an instruction mapper to convert the intermediate representation into a target platform instruction; identifying a target architecture type through a platform identification code; designing a test case to verify a compiling result, and deploying the optimized target program to a target platform; according to the method and the device, the expandability and the code quality of the compiling tool chain can be improved by constructing the unified intermediate presentation layer and the optimization framework, and more efficient cross-platform support is realized.

A computer model is monitored during operation to evaluate performance of the model with respect to different groups evaluated by the model. Performance for each group is evaluated to determine an inter-group performance metric describing how model predictions across groups differs. A threshold for excess inter-group performance differences can be calibrated using withheld training data or out-of-time data to provide a statistical guarantee for detecting meaningful variation in inter-group performance metric differences. When the inter-group performance exceeds the threshold, the computer model may be considered to deviate from expected behavior and the monitoring can act to correct its operation, for example, by modifying actions that may otherwise occur due to model predictions or by initiating model retraining.

A server computer system comprises a communications module; at least one processor coupled with the communications module; and a memory coupled to the processor and storing processor-executable instructions which, when executed by the at least one processor, configure the at least one processor to provide a plug-in to an interface design software application executing on a computing device, the plug-in allowing the server computer system to communicate with the computing device to monitor design of an interface within the interface design software application; generate at least one recommendation for tagging at least one element of the interface; and send, for display on a display screen of the computing device within the interface design software application, the at least one recommendation for tagging the at least one element.

The technology evaluates the compliance of an AI application with predefined vector constraints. The technology employs multiple specialized models trained to identify specific types of non-compliance with the vector constraints within AI-generated responses. One or more models evaluate the existence of certain patterns within responses generated by an AI model by analyzing the representation of the attributes within the responses. Additionally, one or more models can identify vector representations of alphanumeric characters in the AI model's response by assessing the alphanumeric character's proximate locations, frequency, and / or associations with other alphanumeric characters. Moreover, one or more models can determine indicators of vector alignment between the vector representations of the AI model's response and the vector representations of the predetermined characters by measuring differences in the direction or magnitude of the vector representations.

A system is disclosed that uses expert systems to monitor and evaluate quality in a large language model. The system can include a prompt library that associates prompts with areas of expertise. The system selects prompts from the library and evaluates first responses generated by a large language model for the set of prompts against second responses generated by a modified version of the large language model to the set of prompts. The evaluation uses expert systems associated with the areas of expertise for the set of prompts. If the system determines that the evaluation indicates a degradation criterion is met, the system may take remedial action. The system provides an effective way to evaluate and prevent the use of modified language models that do not meet the required standards.

The systems and methods disclosed herein receives artifacts generated using a first set of models within a multi-model superstructure. The multi-model superstructure includes a second set of models to test the first set of models. The multi-model superstructure dynamically routes the artifacts of the first set of models to one or more models of the second set of models by (i) determining a set of dimensions of the artifacts against which to evaluate the artifacts and (ii) identifying the models in the second set used to test the particular dimension. The second set of models then assesses each artifact against a set of assessment metrics. If an artifact fails to meet one or more assessment metrics, the second set of models generates actions to align the artifact with the set of assessment metrics.

Apparatuses, systems, and methods for scalable 1-pass error correction code operations. A memory device includes an error correction code (ECC) circuit which generates a number of parity bits based on a plurality of data bits during a write operation. The number of parity bits may be selected based on a setting in a mode register. The data and parity are written to the memory array as part of a single access pass. The data may be written to a selected portion of the data column planes, while the parity is written to one or more column planes of the extra column plane or a non-selected portion of the data column planes.

The embodiment of the invention provides a cross-platform compatibility automatic testing method, which comprises the following steps: scanning a target platform architecture specification file to extract instruction set features, collecting chip platform information to construct a testing environment database, constructing a testing case feature model according to the instruction set features and the testing environment database, designing a test case generator based on the test case feature model, and generating a test task scheduling scheme based on the test execution framework; inputting the abnormal behavior characteristics and the performance baseline threshold value into a test result analyzer, constructing a scoring dimension containing functional integrity, a performance standard-reaching rate and a stability index, and mapping the abnormal behavior characteristics and the performance baseline threshold value to the scoring dimension to generate a test result scoring matrix, calculating a weighted average value of the test result scoring matrix and the optimized weight coefficient as a compatibility level, generating a test report based on the compatibility level, and executing regression verification; according to the invention, the comprehensiveness and accuracy of the test can be improved, and more efficient compatibility evaluation is realized.

A memory device can be coupled to a host device using a compute express link (CXL) interconnect. The memory device can include a host interface circuit and processing logic circuitry, such as a subsystem manager circuit. The host interface circuit of the memory device can be configured to operate in one of an autonomous mode and a distribution mode. In the autonomous mode, the host interface circuit is configured to maintain a connection between the host device and the memory device while the subsystem manager circuit of the memory device is unavailable, such as when the subsystem manager circuit undergoes a reset routine. In the distribution mode, the host interface circuit is configured to allow communication between the host device and the subsystem manager circuit of the memory device.

The present application provides a vulnerability remediation code identification method, comprising: acquiring commit information of open-source software, the commit information comprising a committed code snippet and text information associated with the code snippet; inputting the commit information into a first language model and performing identification to obtain function change information and test case information; inputting the function change information into a vulnerability remediation identification model to obtain a vulnerability remediation code snippet for a target vulnerability, and inputting the test case information into a vulnerability test case identification model to obtain a test case code snippet for the target vulnerability; and inputting the vulnerability remediation code snippet for the target vulnerability and the test case code snippet for the target vulnerability into a second language model and performing integration and identification to obtain an identification result. On the basis of notable characteristics in a test case for a vulnerability, the identification rate of vulnerability remediation code can be effectively increased. In this way, a user can discover potential vulnerabilities in advance, thus reducing security risks.

In one aspect, a method for penetration testing for optimization of network security policies is disclosed. The method includes determining, by a security management service, that one or more cybersecurity threats successfully penetrated a security service protecting a pseudo-target in a penetration testing environment, analyzing, by the security management service, the one or more cybersecurity threats that successfully penetrated the security service to characterize the one or more cybersecurity threats, and generating, by the security management service, an update of a policy used by the security service that would prevent the one or more cybersecurity threats from penetrating the security service based on the analysis of the one or cybersecurity threats.

Systems and methods are described for building artificial intelligence (“AI”) pipelines. A user interface (UI) includes selectable pipeline objects, such as a dataset and an AI model, that a user can position and connect on the screen. This causes execution linking between the selected pipeline objects, with the execution linking being visually displayed in the UI. A management policy can be applied to the pipeline, including user or device requirements for accessing the dataset in the pipeline. Then the UI can present a simulated execution of the AI pipeline, in which a test query is input and the pipeline objects execute in an order displayed in the UI. The pipeline can then be deployed for access at an endpoint.

Activity monitoring of a cloud compute environment based on container orchestration data may be performed by a data platform. For example, the data platform may obtain audit log data generated by a container orchestrator within a cloud compute environment, generate a data model based on the audit log data, and use the data model to monitor the activity for a security issue. The data model may be indicative of activity occurring with respect to one or more containerized applications executing within the cloud compute environment and the monitored activity may be activity with respect to the one or more containerized applications. Corresponding methods, systems, and products are also disclosed.

Techniques are described for a computing system configured to obtain a plurality of candidate logs for a plurality of layers of a computing infrastructure. The computing system may, for each candidate log of the plurality of candidate logs, map the candidate log to a log template of a plurality of log templates, wherein each log template to which a candidate log is mapped is a mapped log template. The computing system may rank mapped log templates based on properties of the mapped log templates. The computing system may select, based on the ranking, one or more candidate logs as critical logs. The computing system may output at least one of (1) an indication of the critical logs to determine a potential root cause associated with a performance issue of a network application or (2) an indication of the potential root cause associated with the performance issue of the network application.

The invention relates to a code generation method and system based on heterogeneous multi-agent collaboration and electronic equipment. According to the method, a special commentator agent instantiated by a heterogeneous large language model is added into a multi-agent collaboration process; according to the method, consistency and feasibility verification is carried out on a demand document given by an analyst agent by adopting a commentator agent, a modification suggestion is proposed in an early stage, deviation and problems are found in time in the early stage of a task, the cost and complexity of later correction are reduced, the robustness and reliability of codes are improved, and the method is suitable for large-scale popularization and application. And the generated result can still maintain high-level correctness under extreme conditions or specific boundary conditions. And the method has practical significance in improving software development efficiency, reducing project risks and improving the quality of an intelligent automatic solution.

A system and a method for automatically discovering and managing actions in an application is disclosed. The system includes a data ingestion layer for receiving application data from multiple sources, a scanning and systematic traversal engine for interacting with UI elements and capturing network calls, an action mapping and generation module for correlating UI actions with API calls and categorizing actions, an AI-driven icon and description generator for creating visual representations and textual descriptions of actions, a user interface for displaying and modifying discovered actions, and a continuous monitoring component for triggering re-scanning based on coverage metrics, error detection, or version updates. The system employs synthetic data generation and AI-driven exploration to uncover hidden or undocumented APIs, enabling comprehensive mapping of an application's capabilities at the API level.

The invention provides a rules engine that evaluates, modifies, and dynamically routes inputs to AI models based on provider restrictions and management rules. Provider restrictions are stored. Based on management rules and the detected provider restrictions, the input can be modified with contextual placeholders and prompts can be injected for input into the destination model. Reversal of the modifications can occur to the outputs. The system can notify the user, an administrator, and a supervisor regarding the security evaluation and remedial actions. The evaluations can be logged for auditing purposes.

The invention discloses an intelligent software testing method and system based on automatic script generation, and relates to the technical field of software testing, and the method comprises the following steps: deploying a distributed data acquisition probe, dynamically adjusting sampling frequency, collecting software operation data, and extracting data features based on a deep learning model; based on the data features, establishing a behavior pattern classification system, and mapping behavior patterns to realize hierarchical behavior classification; the method comprises the steps of constructing a test scene, generating test data, executing a test task, monitoring a test execution state in real time, calculating a test efficiency index, evaluating and feeding back a test effect, and dynamically adjusting a feature extraction strategy. According to the method, deep learning and reinforcement learning technologies are utilized, accurate recognition and classification of software behavior modes are achieved, and comprehensiveness and effectiveness of testing are guaranteed through multi-dimensional coverage rate analysis and a closed-loop optimization mechanism; and meanwhile, the automation level and the intelligent degree of software testing are remarkably improved, and a powerful guarantee is provided for improving the software quality.

The invention discloses a software automatic testing system based on generative artificial intelligence, which is characterized in that a software analysis module identifies attribute information of a UI component based on a multi-modal large model for a test object, and constructs a UI component knowledge graph according to a structured information document; the knowledge retrieval module receives the test requirements and retrieves related historical test cases, test scripts and related test data; the organization interaction module sends a test intention and demand information to the knowledge retrieval module for retrieval according to the input user test intention, and test demand knowledge is returned; sending the UI component knowledge graph to the software analysis module for searching the knowledge graph of the test object, and returning the UI component knowledge graph; a test generation module receives test demand knowledge and the UI component knowledge graph, and generates a test case and a test script; and the script execution module receives the test case and the test script, starts a test process and records a test result.

Aspects of the disclosure include methods and systems for performing automated fault scenario generation for chaos engineering. Aspects include obtaining a configuration of a service under test, obtaining a first plurality of fault scenarios, and applying each of the first plurality of fault scenarios to the service under test. Aspects also include recording telemetry data regarding an operation of the service under test under each of the fault scenarios, selecting, based on the telemetry data, a first fault scenario from the fault scenarios, and generating a second plurality of fault scenarios. Aspects further include applying each of the second plurality of fault scenarios to the service under test, recording telemetry data regarding the operation of the service under test under each of the second plurality of fault scenarios, and identifying a vulnerability of the service under test based on the recorded telemetry data.

Methods and systems for artificial intelligence (Al) assisted automation of testing of a software functionality related to a user intent on a software platform is provided. In one embodiment, the method includes receiving user action data indicating the user intent related to a software platform and generating a human-readable test scenario. The test scenario, which includes a sequence of human-readable testing steps and an expected outcome, is generated using a scenario machine learning (ML) model. The method includes generating an interpretable test script based on the test scenario using a script ML model. The test script is interpreted to implement testing steps on the software platform, generating a test outcome. A human-readable test report is also generated to evaluate the test scenario on the software platform, based on the test outcome and the expected outcome.

The invention provides a security data penetration test method. The method comprises the steps of pre-establishing a multi-thread application program race condition vulnerability feature library, performing static analysis on source codes of a target application program, identifying potential race condition vulnerability points, and obtaining a preliminary vulnerability candidate set; for monitored suspected race condition events, abstracting event context information into constraint conditions, exploring an input combination of suspected static conditions by using a constraint solver, and determining a minimum input set of stable triggering race conditions; aiming at memory models and synchronous primitives of different programming languages, obtaining a race condition inverse mode, and extracting universal utilization primitives covering various race condition vulnerabilities; in the penetration test process, static analysis, dynamic monitoring and symbolic execution are applied to mine race condition vulnerabilities of a target application system, the availability of the vulnerabilities is verified, and evaluation of system security is assisted to be completed.

The systems and methods disclosed herein obtain a set of alphanumeric characters defining constraints for agents and the agents' operational data. Each agent uses an output from a first set of artificial intelligence (AI) models and predefined objectives to autonomously generate proposed actions for execution on software application(s). For each agent, a second set of AI models evaluates the agent by identifying gaps in the proposed actions by comparing them with the expected actions. Using a third set of AI models and the identified gaps, the systems modify the proposed actions by adding, altering, or removing actions from the proposed actions.

Techniques for leveraging a large language model (LLM) in software development are described. A description of an error message associated with a software system hosted by the multi-tenant provider network is received. An LLM is prompted with an analysis prompt to generate an analysis of the error message, the analysis prompt including the description of the error message, and the analysis of the error message is received from the LLM. The LLM is prompted with a suggested resolution prompt to generate a suggested resolution to a cause of the error message, the suggested resolution prompt including the description of the error message and the analysis of the error message, and the suggested resolution is received from the LLM. A change to resolve the cause of the error message is sent to an originator of the received description, the change based at least in part on the suggested resolution.

Systems, methods, and devices that relate to monitoring and managing autonomous agents are disclosed. In one example aspect, the method includes receiving activity data from autonomous agents in an operational environment, deploying static and dynamic observing agents to monitor expected behavior and deviations, detecting a deviation by an autonomous agent, determining the cause through analysis, performing a mitigative action based on the cause, and executing a preventative action to block similar future deviations. The method may also involve configuring observing agents with different observation modalities, periodically modifying observation parameters unpredictably, facilitating direct communication between observing agents, resolving conflicts in observations, and updating observation policies. Mitigative actions can include disabling credentials, rerouting communications, and logging actions. Preventative measures may involve updating behavioral policies and adjusting agent parameters to disincentivize problematic behaviors.

The invention provides a test case generation method and system based on a large model and retrieval enhancement generation, and the method comprises the steps: obtaining a software description document, carrying out text fragment segmentation and vectorization, and constructing a software knowledge base; obtaining and vectorizing a demand document, and recalling a content optimization demand document in the reference software knowledge base based on the retrieval enhancement generation framework; constructing a first Prompt instruction, and splitting the optimized demand document into demand fragments of different test points by using a large language model; and finally, constructing a second Prompt instruction for the demand fragment of each test point, generating a test case by using a large language model, and establishing a mapping relationship between each test point and the test case for linkage output. According to the method, the understanding ability of the large model on the demand document is effectively enhanced; meanwhile, the requirements are decomposed into test points, mapping is carried out on the test points and related test cases, and completeness and accuracy of the requirements and the test cases can be conveniently sorted.

The invention relates to a fuzzy testing method, device and equipment based on a large language model, and the method comprises the steps: analyzing and analyzing RFC standard documents of various network protocols through the large language model, and extracting protocol rules and constraint conditions; according to the protocol rule and the constraint condition, a large language model driving method is adopted to generate a test case conforming to protocol characteristics; and performing a differential test through the test case, and verifying the consistency between the actual software of the network protocol and the RFC standard document. According to the invention, the accuracy and security of implementation of the complex network protocol can be improved, and the test efficiency and coverage rate are optimized.