3336 results about "Program code" patented technology

Systems and methods for building an application incorporating AI functionality include enabling access to a developer application framework associated with a SaaS platform, receiving application code for generating an application, and receiving a selection of an AI assistant add-on provided by the SaaS platform. A link is established between the selected AI assistant add-on and a SaaS platform element, permission is provided to access data from the at least one of the plurality of linked SaaS platform elements. Based on the permissions, data is transferred from the at least one of the plurality of linked SaaS platform elements, and AI integration code is received including at least one reference to the transferred data and at least one call to an AI agent. The application is published for selective use with SaaS platform elements and is configured to exchange data with the at least one of the plurality of SaaS platform elements.

A generative artificial intelligence (AI) driven code fixing pipeline has been created that uses a transformer-based large language model (LLM) to patch flawed program code. A pre-trained LLM is fine-tuned to generate a response that is a modified version of a code fragment in a prompt to the pre-trained model. After fine-tuning, the pre-trained LLM (hereinafter “code fix model”) is integrated into a pipeline that includes a program code cybersecurity scanner and a prompt generator. The scanner generates indications of flaws in program code and weakness types for those flaws. These indications flow into the prompt generator. The prompt generator retrieves reference code pairs based on weakness type and programming language to generate a batch of prompts to run inference on with the code fix model. The responses generated by the code fix model are presented as patching alternatives.

Disclosed herein are computer-implemented systems and methods for code value assessment. For example, in one aspect, the system comprises, an input module configured to receive program code submissions from developers, a processing unit equipped with Text-Based Models with Contextual Understanding (TBM-CUs) configured to evaluate the functional meaning, purpose, and value of submitted code segments and distinguish between code contributions from human programmers and machine learning systems, a visualization module configured to present the assessed value of the analyzed code over various time periods and dimensions, offering insights into trends, patterns, and comparative performance, a communication module configured to translate programming code meaning or function into plain language summaries for non-technical stakeholders, and an AI peer review module configured to automatically review, accept or reject code contributions.

In one embodiment, a computer-implemented method executed using one or more processors of an incident management system comprises receiving a notification of an incident associated with a computer system, and in response to receiving the notification: extracting an error message from the notification; reading a set of computer program code changes that have been implemented in the computer system; matching the error message to the set of computer program code changes and outputting a set of one or more candidate code changes that may correspond to the error message; based on the set of candidate code changes, generating an automatic remediation for the incident; and using the one or more processors of the incident management system, executing the automatic remediation on the computer system.

A method includes generating a modeling language diagram that is indicative of a compiled version of program code. The modeling language diagram includes at least one node corresponding to at least one function in the program code. The method also includes identifying at least one function signature associated with anode state transition between nodes in the modeling language diagram. The method also includes identifying, using a large language model, a particular policy to attribute to the at least one function signature associated with the node state transition. The method further includes performing, using the large language model, a policy compliance operation that ensures a particular portion of the program code complies with the particular policy. The particular portion of the program code is associated with the node state transition. Performing the policy compliance operation includes generating a policy-compliant version of the particular portion of the program code.

Behaviour Driven Development (BDD) is used to define test scenarios and to write the test scenarios in code first before writing the application code. Expected behaviours of the new functionality in natural language are defined and converted into test code driven by a Domain Specific Language (DSL) that encapsulates the description of the expected behaviour, after which code for the functionality can start to be written. In certain implementations, artificial intelligence (AI) may be used to first generate the test scenarios and relevant test data, then to produce code that passes all the tests (or iterates until it does) such that the code requires zero or nearly zero human inspection. If generated code passes the test scenarios, it meets the expected behaviour. This saves substantial time investment and reduces the number of steps to completion.

Disclosed herein are systems and methods for improving the auto-generation of pipelined search query statements by a large language model (LLM). In some examples, such a method includes operations of receiving a user-provided prompt, wherein the user-provided prompt is provided in natural language, identifying an objective of the user-provided prompt, and based on the objective, providing the user-provided prompt to a first operational pipeline of a plurality of operational pipelines, wherein each operational pipeline is associated with a unique prompt template. Additionally, the method may include generating, by the first pipeline, an auto-generated prompt based on a first unique prompt template of the first pipeline, providing the auto-generated prompt to a large language model (LLM), and receiving a response to the auto-generated prompt from the LLM. A graphical user interface (GUI) may then be generated that displays the response to the auto-generated prompt.

The invention relates to the technical field of ultra-low power consumption microcontrollers, and discloses an ultra-low power consumption microcontroller architecture design method and system, and the method comprises the steps: constructing a heterogeneous dual-core processor comprising a high-performance core and a low-power consumption core, and dividing the heterogeneous dual-core processor into a plurality of independent power domains; constructing a sub-threshold voltage operation system on a plurality of independent power supply domains, and configuring a multi-layer storage structure in the heterogeneous dual-core processor; recording task characteristic data by using a multi-level storage structure, executing task dynamic allocation between dual cores, and creating a dynamic task allocation mechanism; and performing static analysis and function block division on the application code based on a dynamic task allocation mechanism to obtain a plurality of function code blocks, and performing storage hierarchy and processor core allocation on the plurality of function code blocks to obtain a function code allocation result. According to the method, the dynamic migration of the workload and the optimal resource allocation are realized, and the energy consumption of the microcontroller in various working scenes is reduced.

The invention discloses a network security vulnerability identification and repair method and system, a medium and a program product, and the method comprises the steps: segmenting a system behavior sequence into a plurality of subsequences; constructing a behavior dependency graph; calculating functional similarity, and dividing the functional similarity into node clusters; calculating a behavior consistency score, and determining a potential vulnerability region; determining call chain information; constructing a vulnerability propagation path, and calculating criticality; dividing first-level hazard vulnerabilities, second-level hazard vulnerabilities and third-level hazard vulnerabilities according to the criticality; determining a repair priority, and generating a vulnerability report; according to the vulnerability types, the repair priorities and the propagation path information, corresponding repair strategies are sequentially matched from a preset repair template library according to the sequence of the repair priorities; and performing hierarchical repair on the application code based on the repair strategy obtained by matching to obtain a repair code, and deploying the repair code to the target system. According to the invention, the accuracy and efficiency of identifying and repairing network security vulnerabilities are improved.

Certain aspects of the disclosure are directed to searching programming code repositories. In examples, a method is provided that includes receiving a natural language search query via a graphical user interface; converting the natural language search query into a vector representation; determining a proximity score between the vector representation of the natural language search query and at least one vector representation corresponding to a machine learning model generated natural language description of a programming code segment; and providing a search result corresponding to the programming code segment based on the proximity score.

Systems and methods for efficiently translating program code from a source language to a target language. Input source code is parsed, using a processor device, into an Intermediate Representation (IR). A structural and semantic model of the source code are established by applying static analysis to the IR, and a program skeleton of the target code is constructed from the IR, including generating context-aware placeholders. The IR is transformed into a Single Static Assignment (SSA) form, and a System Dependency Graph (SDG) is built from the SSA form. The SDG is traversed to order translation tasks, and ordered tasks are translated into the target language using a Large Language Model (LLM). A translated program is generated by integrating translated code segments into a coherent program structure in the target language.

An end-to-end approach for (i) identifying potential vulnerabilities in software applications, including security vulnerabilities, (ii) verifying / confirming the potential vulnerabilities as actual vulnerabilities and (iii) in response, identifying the necessary remedial actions necessary to eliminate or at least mitigate the vulnerabilities. An intelligent agent is implemented that is configured to detect a change to the application's code or computing environment and, as a result of detection of changes to the code or computing environment, identify potential vulnerabilities, verify / confirm the potential vulnerabilities as actual vulnerabilities through determination / creation of vulnerability test cases and automatically identify the necessary remedial actions necessary to eliminate or mitigate the vulnerabilities.

Apparatuses, systems, and techniques to select optimizations to be performed by compilers. In at least one embodiment, a processor includes one or more circuits to perform a compiler to select one or more optimizations to one or more first versions of a program based, at least in part, on a result of performing said one or more optimizations on one or more second versions of said program.

Provided is a terminal for transmitting information. The terminal includes a memory and a processor, wherein the memory stores at least one program code, and the processor is configured to load and execute the at least one program code, to cause the terminal to: report assistance information in a case that a first condition is satisfied, wherein the assistance information indicates service information of the terminal, the service information comprising at least one of a service characteristic of the terminal, remaining service information of the terminal, or remaining data information of the terminal.

One embodiment of a method for generating program code to control a robot includes receiving user input specifying a task to be performed by the robot, processing the user input via a first machine learning model to generate a plurality of subtasks for performing the task, and for each subtask included in the plurality of subtasks, processing the subtask via a second machine learning model to generate program code for controlling the robot to perform the subtask.

Computer implemented methods, systems, and computer program products include program code executing on a processor(s) obtains queries from sources via a query interface. The program code analyzes the queries to obtain attributes of the queries. The program code applies a trained classifier to the attributes to classify the queries into finite categories based on a predicted usage of a given resource type exceeding a pre-determined threshold and an anticipated execution duration for each query. The program code determines, based on the finite categories of the one or more queries and resource configurations of resources comprising the database service, which resource of the resources can efficiently execute each query of the one or more queries. The program code schedules each query of the one or more queries for execution by the determined resources.

Methods and systems are presented for providing a large language model-based query optimizer to interface between program developers and database systems. The query optimizer receives programming code corresponding to a set of queries intended for a database system from a program developer. The query optimizer then uses a machine learning model to analyze the programming code and to determine a set of strategies for executing the set of data queries corresponding to the programming code. To determine the set of strategies, the machine learning model analyzes dependencies among the set of data queries and retrieves sample data from the database system. The machine learning model implement the set of strategies by incorporating additional instructions in the programming code for the database system such that the database system would execute the set of data queries according to the set of strategies.

Automatic code generation for robotic process automation (RPA) that uses a cognitive artificial intelligence (AI) layer to automatically generate computer program code, text, or other items based on an input source is disclosed. Expressions and / or code snippets may be generated for RPA workflows that provide code in a programming language or convert code from one language to another. The input source may include natural language sentence(s), source document(s), pseudocode written by a user for a desired task, etc. These inputs may be used to modify an RPA workflow, generate a new RPA workflow, create a document describing a task, generate an application, etc.

An apparatus comprising: at least one processor; and at least one memory including computer program code; wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to: determine whether to report a power headroom report value to a network node, the power headroom report value being applicable to a mixed slot where simultaneous transmissions in downlink and uplink are scheduled in non-overlapping sub-bands of the same carrier; wherein the power headroom report value is based on a difference between an uplink transmit power given an expected cross link interference for a user equipment, and an uplink transmit power at the mixed slot; and determine whether to reduce the uplink transmit power at the mixed slot, based on information received from the network node.

The invention provides a carrying task equipment scheduling method, a scheduling server and a computer readable storage medium. The method comprises the following steps: in response to a carrying task triggered by an upper system, splitting the carrying task according to a required equipment category of the carrying task to obtain a plurality of subtasks with serial numbers; executing a plurality of subtasks in sequence, and determining an optimal carrying path of the current subtask; determining target carrying equipment of the current sub-task based on the target equipment category of the current sub-task and the working state of each carrying equipment in the operation area; calling a program coding plug-in of the target carrying equipment to code the preferable carrying path to obtain first equipment coding information of the current subtask; and calling an interface of the target carrying equipment to push the first equipment coding information to the target carrying equipment so as to schedule the target carrying equipment to execute the current subtask according to the preferred carrying path. The carrying path and the carrying equipment of the carrying task can be planned in real time, and the cargo carrying efficiency is improved.

The invention belongs to the technical field of industrial automation and artificial intelligence crossing, and particularly relates to an AI industrial control intelligent master system and method based on deep learning. The system comprises an intelligent programming module, a real-time monitoring module, a fault management module, an optimization decision module and a man-machine interaction module. The system automatically generates a PLC program code through a deep learning model, monitors an equipment operation state through a multi-dimensional anomaly detection technology, analyzes a fault mode and a propagation path by using a knowledge graph and a graph neural network, optimizes process parameters and a maintenance strategy based on digital twinning and reinforcement learning, provides a 3D virtual interface and an AR remote cooperation function, and improves the reliability of the system. And intelligent management of the whole life cycle of the industrial control system is realized. The PLC programming efficiency is greatly improved, accurate anomaly detection and fault prediction are realized, system operation parameters are optimized, the service life of equipment is prolonged, the man-machine interaction experience is improved, and the problem of unified management of multiple brands of PLCs is solved.

The invention provides a software code defect detection method and system based on program code feature fusion, and the method comprises the steps: carrying out the grammar and structure inspection of a code through a static analysis tool, and extracting defect features related to control flow chaos, exception handling and resource leakage; running a preset test case through a dynamic analysis tool, capturing an execution path and an abnormal behavior in a runtime state, and obtaining defect features which are not covered by static analysis; if the output result of the decision tree model is that defects exist, grouping comprehensive defect feature vectors by adopting a density-based clustering algorithm, determining the types and distribution ranges of the defects, and selecting the density clustering algorithm to process defect features in non-spherical distribution; and according to a clustering result, generating a defect repairing suggestion, combining code logic and execution path information, and determining a specific position and an influence range of the defect. According to the method, the stealth defect in the code can be effectively identified and positioned, the software quality and reliability are improved, and timely and accurate defect repairing guidance is provided for developers.

The invention discloses an automatic compiling and adapting method for an RISC-V extension instruction, and belongs to the technical field of compilers. According to the method, a dynamic DSL (Digital Subscriber Line) for registering a custom instruction, adding register use constraints and defining a specific code mode is designed. The LLVM plug-in is used for automatically integrating the registered custom instruction in the compiling process. The invention relates to a self-defined instruction rapid adaptation mechanism which is specially provided for a continuously provided RISC-V self-defined instruction expansion scene, and by automatically generating correct assembly codes of registered self-defined instructions, correct registers are automatically distributed and reserved according to register use constraints; the method comprises the following steps of: searching a defined specific code mode and semantics, and automatically generating and inserting a registered custom instruction in a program, so as to realize the rapid adaptation of a compiler to an RISC-V custom extension instruction and the automatic use of a program code to the custom instruction, and the whole process does not need a developer to manually modify the compiler or a program source code.

The invention provides a container protection method and device. A first operating system and a confidential virtual machine are installed in a server. And deploying a partition container in the confidential virtual machine, wherein the partition container is used for running the load of the application. And deploying a shadow container outside the confidential virtual machine, wherein the shadow container does not run the load of the application. A server receives a first request sent by an application on an electronic device for implementing a first function. In response to the request, the server calls a resource to a partition container in the confidential virtual machine through a shadow container outside the confidential virtual machine, and the partition container realizes a first function according to the resource. Therefore, the shadow container outside the confidential virtual machine is used for acting on the partition container in the confidential virtual machine to obtain the resources on the first operating system, program codes used for calling the resources on the first operating system in the partition container can be saved, program codes in the confidential virtual machine are simplified, and the operation efficiency of the confidential virtual machine is improved. The security risk caused by deploying a large number of programs in the confidential virtual machine is effectively reduced, and the system security is improved.

The invention discloses natural language computer-aided engineering and a computer-aided design method. CAE simulation analysis and design optimization on complex product design are high in threshold and difficulty. According to the method, an artificial intelligence large language model is finely adjusted by using professional knowledge of CAE and CAD, an agent is developed to call an API service of the large language model, an engineer dialogues with the agent by using a natural language, and the large language model can understand purposes and requirements of the engineer and generate corresponding operation instructions or program codes of CAE software and CAD software; the agent drives the CAE software and the CAD software to execute instructions or run programs to complete simulation analysis and design optimization tasks, and an engineer can cooperate with a plurality of professional CAE software and CAD software to perform simulation analysis and design optimization of multi-physics coupling only by dialogue with the agent. The intelligent agent can autonomously complete loop iteration simulation analysis and design optimization tasks according to formulated scheme steps and rules.

The invention discloses a vehicle-mounted tire pressure sensor rapid programming and automatic positioning method and related equipment, and the method comprises the following steps: respectively transmitting low-frequency programming signals to a preset number of tire pressure sensors; controlling a tire pressure sensor to transmit a high-frequency response signal to a tire pressure receiver; writing the high-frequency response signal into a program code corresponding to the tire pressure sensor, and generating a one-to-one corresponding unique identity; respectively transmitting low-frequency positioning signals to a preset number of tire pressure sensors; generating status words in one-to-one correspondence according to a preset corresponding relation and the unique identity; a preset number of tire pressure sensors are controlled to send corresponding status words to a tire pressure receiver; controlling the tire pressure sensor to identify the status word to obtain corresponding real-time pressure data, and determining position information corresponding to the tire pressure sensor according to the unique identity; and displaying the real-time pressure data and the position information corresponding to the preset number of tire pressure sensors in a preset display screen. The method is used for improving the programming efficiency and the positioning accuracy of the tire pressure sensor.

Disclosed herein are systems and methods for improving the auto-generation of pipelined search query statements by a large language model (LLM) through a post processing. In some examples, such a method includes operations of receiving, by a post processing engine, a response to an auto-generated prompt from the LLM that includes programming code generated by the LLM, performing a post processing of a response from the LLM that includes the programming code generated by the LLM including performing an error correction process when a term of the programming code generated by the LLM is inconsistent with terms of a schema of the user, and generating a graphical user interface (GUI) that displays the response to the auto-generated prompt when the terms of the programming code generated by the LLM including any replacement terms are consistent with the terms of the schema.

An apparatus for measuring the stopping distance of a working machine is provided. The working machine includes a user interface, at least one processor, and at least one memory including a computer program code. The at least one memory and the computer code are configured to, with the at least one processor, cause the apparatus at least to: receive at least one speed parameter based on at least one user input on the user interface, receive at least one speed information value from a controller or the engine control unit of the working machine, send at least one command to the controller or the engine control unit to at least stop the working machine when the received speed information value is equal to the at least one speed parameter, and calculate the stopping distance of the working machine. A method and a system are also disclosed.

Disclosed herein are systems and methods for improving the auto-generation of pipelined search query statements by a large language model (LLM) through novel processes for performing retrieval augmented generation (RAG). In some examples, such a method includes operations of receiving a natural language user-provided prompt, performing a first RAG process including retrieving first RAG data including natural language, programming syntax pairings from a vector database and performing a second RAG process including retrieving second RAG data including additional programming syntax examples from a vector database that have a second level of similarity with programming syntax of the natural language, programming syntax pairings retrieved during the first RAG process. The method may also include generating an auto-generated prompt requesting generation of programming code by a large language model (LLM) and generating a graphical user interface that displays the response to the auto-generated prompt from the LLM.

The invention relates to a method for diagnosing a vehicle system of a vehicle, having the following steps: - providing (S1) a system configuration of the vehicle system at least with the program code of the software functions implemented in at least one control device; - providing (S2) a sequence of vehicle events during past operation of the vehicle; - evaluating (S3) the system configuration and sequence of vehicle events using a trained large language model to obtain the cause of an error or a predicted error, wherein the large language model is trained on the basis of a plurality of automotive and non-automotive program codes and a plurality of sequences of vehicle events for different vehicles; and - carrying out (S4) error handling as a function of the cause of the error or of the predicted error.