4645results about "Creation/generation of source code" patented technology

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.

A computer-implemented method is disclosed. The method includes: obtaining web content of a webpage; determining whether the web content complies with defined first requirements for user accessibility; in response to determining that the web content does not comply with the first requirements: identifying non-compliant web content elements of the webpage; and presenting, via a user interface of a source code editor: indications of first portions of source code for the webpage corresponding to the non-compliant web content elements; and a user interface element representing an action for altering one or more of the non-compliant web content elements.

A computer-implemented method is disclosed. The method includes: identifying, in a graphical user interface, a first user interface element and a second user interface element, the first and second user interface elements being of different types and associated with a first group of user interface elements; receiving, via a keyboard, input of a command to trigger a first interaction with both the first and second user interface elements; determining that the first interaction is not defined for one of the first and second user interface elements; and performing a related action associated with the first interaction to the one of the first and second user interface elements.

Methods and systems for generating a sharable script related to an input digital model on a digital platform are provided. The method includes receiving a user request indicative of a digital task involving an input digital model, and retrieving a corresponding input digital model file. Then, determining characteristic attributes of the input digital model, where the characteristic attributes include digital artifacts generated from the input digital model file. Then, selecting from a collection of templates, using a machine learning (ML) engine, a template matching the characteristic attributes of the input digital model. The ML engine may be trained on documentations of digital tools integrated into the digital platform, a resource-capability mapping of the digital platform, and sample digital thread orchestration scripts collected through past uses of the digital platform. Finally, the method includes generating the sharable script that implements the digital task, based on the selected template.

The invention discloses an automatic code generation and optimization system based on multiple modes, which relates to the technical field of automatic programming, and comprises a task analysis module for analyzing task description and constraint conditions in combination with a CLIPS rule engine and a knowledge graph and original data, identifying task targets and requirements, and outputting a task risk assessment report and a task intention set; the optimization decision module is used for selecting an optimal modal data subset by using a particle swarm optimization algorithm and a path planning algorithm, performing optimization adjustment according to task requirements, and outputting a code generation strategy; and the test evaluation module is used for evaluating and improving the unit test and the integration test by utilizing the variation test, carrying out quality and performance evaluation on the code through continuous integration and continuous delivery, and outputting a code evaluation report and an optimization suggestion. According to the method, the optimal modal data subset is dynamically screened, and the data transmission path is optimized, so that the code performance is ensured, the computing resource consumption is reduced, and the global optimization of the code generation strategy is realized.

Computer-implemented methods are included for training an autonomous machine to perform a target operation in a target environment. The methods include receiving a natural language description of the target operation and a natural language description of the target environment. The methods further include generating a prompt such as a reward and / or goal position signature by combining the natural language description of a target task or goal and the natural language description of the target environment. The methods then generate a reward or goal position function by prompting a large language model with the generated prompt. The methods further include computing a state description using a model of the target environment, and training a policy for the autonomous machine to perform the target task or goal using the generated function and state description.

The systems and methods provide a model deployment criterion. The model deployment criterion indicates a difference in a value against which the proxy model may be measured to determine when, if ever, the proxy model should be deployed to replace the existing rule engine. The model deployment criterion may be keyed to the proxy model (e.g., based on a difference in its size, throughput speed, number of changes, etc.), the existing rule engine (e.g., based on a difference in its age, update occurrences to its rule base, etc.), and / or comparisons between models (e.g., based on differences in results, throughput speed, efficiency, etc.).

Systems and methods for generating code for artificial intelligence models without requiring training data that is specific to model components and objectives. For example, the system may receive an original version of a rule engine. The system may input the original version, using a first input condition, into a regeneration model to generate a first regenerated version of the rule engine. The system may determine whether the first regenerated version includes a first hallucination based on comparing the first regenerated version to alternative versions of the rule engine, wherein each of the alternative versions were generated using a respective alternative input condition. The system may, in response to determining that the first regenerated version includes the first hallucination, determining whether the first hallucination comprises a positive mutation.

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.

The invention relates to the technical field of software development management, in particular to an internet software development management system and method. The method comprises the following steps: collecting an original function demand set and carrying out function clustering analysis; extracting and classifying demand function features and generating independent demand units; carrying out dependency analysis on the independent demand units; constructing a dependency relationship set and a demand logic structure; the method comprises the following steps: generating a demand framework matrix, carrying out organization network simulation to obtain a demand connection network, carrying out code reverse evolution according to the demand connection network, carrying out basic parameter matching to obtain evolution basic parameters, carrying out a pressure simulation test on the evolution basic parameters to generate a pressure test result, and capturing resource pressure defects based on the pressure test result. And optimizing and adjusting the evolution basic parameters to generate optimized development parameters. According to the invention, a more efficient Internet software development management method is realized.

A computer-implemented method for performing natural language-based data integration includes causing execution of a data integration application on a remote device via a network and causing surfacing of a GUI corresponding to the data integration application on a display of the remote device. The method includes receiving, via the GUI, a natural language input representing a data integration task, generating, via an LLM, a set of ordered activities corresponding to the data integration task represented by the natural language input, and selecting, via the LLM, one or more APIs for performing each activity within the set of ordered activities. The method also includes generating a data pipeline based on the set of ordered activities and the API(s) for performing each activity, as well as back-translating the data pipeline to a desired data format for execution by the data integration application.

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.

According to an aspect, a method includes receiving, via an interface, a natural language query about a request for a user device to perform a computer task, generating a prompt including the natural language query and a list of functions, transmitting the prompt to a language model, and receiving a response from the language model, where the response includes machine-readable instructions executable by the user device to perform the computer task. The machine-readable instructions use at least one function from the list of functions. The method includes executing the machine-readable instructions to perform the computer task.

Various embodiments described herein support or provide operations including receiving input data that comprises a collection of data units; determining that the collection of data units corresponds to a number of tokens that exceeds an upper limit of tokens; processing the collection of data units to generate a plurality of outputs; and aggregating the plurality of outputs into a final result provided as output of a function.

The invention discloses an Internet of Things equipment access protocol component development method, which is used for quickly developing an Internet of Things equipment access protocol analysis and processing component at an Internet of Things platform side, and comprises the following steps: S1, constructing a protocol analysis and processing general framework according to the protocol analysis and processing component of an Internet of Things platform; s2, abstracting a specific logic processing part of a protocol in the general framework into a method / function interface; s3, constructing a protocol expert AI knowledge base for data learning; s4, constructing an AI large model and a multi-agent processing flow, selecting a specified large model, configuring related parameters, extracting required parameter extraction / coding logic, and generating an interface implementation code; s5, integrating each generated interface implementation code with the universal framework; and S6, testing, adjusting and optimizing the integrated access protocol analysis and processing component. According to the method, the unified development template can be provided, so that the development technical threshold is reduced, and the development efficiency is greatly improved.

Aspects of the present disclosure relate to automatically updating a software application to ensure compliance with an updated data source. Embodiments include providing an embedding of a first version of a data source and an embedding of a second version of the data source to a comparison engine configured to compare the embedding of the first version of the data source and the embedding of the second version of the data source and generate a data source difference summary. Embodiments further include providing an embedding of a software application code module and the data source difference summary to a code update engine trained to generate an updated version of the software application code module based on the embedding of the software application code module and the data source difference summary. Embodiments further include updating code of the software application using the updated version of the software application code module.

A computer-implemented method is disclosed. The method includes: receiving a request for retrieval of data satisfying one or more criteria, the request including at least one data request parameter; searching a database storing example queries based on the request to identify at least one matching query; providing, to a large language model (LLM), an input prompt to generate a query purporting to retrieve data satisfying the one or more criteria, the input prompt including the at least one data request parameter and the at least one matching query as an example; and receiving, from the LLM, a result including the generated query.

The invention provides a modular construction method of a low-code visual interaction system, and relates to the technical field of low-code development. The method comprises the following steps: constructing an edge cognitive topology network, packaging each node into a reconfigurable meta-module, analyzing a user operation track by using an intention perception interface to generate a meta-intention vector, and carrying out context correlation analysis through a semantic behavior chain to generate a potential behavior sequence. And establishing an asymmetric coupling relationship based on an implicit context mapper, forming a dynamic semantic resonance field, triggering distributed intention resonance and activating a matching element module, and generating a collaborative logic cluster under the constraint of edge computing node resources. And dynamically adjusting an interaction rule and a resource allocation strategy of the logic unit. According to the method, automatic construction and high-precision intention adaptation of complex business logic are realized, the elastic expansion capability and the real-time response efficiency of the system in an edge computing scene are improved, and the risk of logic fault and resource scrambling caused by node isomerism is reduced.

The invention belongs to the technical field of computers, and particularly relates to a zero-code agent construction platform. The system comprises a module database, a semantic analysis and intention decomposition unit, a task flow tree generation unit and an agent construction unit, the semantic analysis and intention decomposition unit is used for performing image language analysis on a natural language instruction input by a user, decomposing the natural language instruction into a plurality of intention units with a sequence, and constructing an intention vector; the task flow tree generation unit is used for generating a plurality of corresponding task flow trees according to the intention vector; and the agent construction unit is used for analyzing all the task flow trees, finding out an optimal task flow tree, calling all target function modules in the task flow tree and completing the construction of the agent. According to the method, the precision of semantic analysis, the flexibility of task logic generation and the automation degree of agent construction are remarkably improved.

The invention relates to a method and a device for generating software based on a large model technology, a medium and electronic equipment, and relates to the technical field of software generation, and the method comprises the following steps: acquiring demand information of a user for software generation; generating a configuration definition file corresponding to the large model generation demand information through a preset code, wherein the configuration definition file is a configuration file of a domain-specific language definition device; performing regular format reformatting processing on the configuration definition file to obtain a processed file; based on the processed file, determining a service flow logic corresponding to a service function in the to-be-generated software, and based on the service flow logic, generating a target software code corresponding to the to-be-generated software through a code generation large model, the format of the target software code being a configuration file format of the domain-specific language definition device; and based on the target software code, through a preset rendering engine, generating a front-end page and a rear-end page of the software needing to be generated. The method has the effect of improving the software development efficiency.

The embodiments are directed to generating source code for a program from a problem description. One or more pre-trained code large language models (LLMs) generate sub-modules from a problem description in a natural language. The sub-modules are filtered based on testing criteria and encoded into sub-module encodings in an embedding space. The sub-module encodings are clustered into multiple clusters. A subset of sub-modules encoding that are close to the centroids of the clusters are selected. The sub-set of sub-modules is decoded into representative sub-modules. The problem description is augmented with the representative sub-modules and fed into one or more pre-trained code LLMs and new sub-modules are generated. The iterations continue until a program is generated from the representative sub-modules.

The invention discloses a data processing method and device, a storage medium and electronic equipment. The method comprises the following steps: when a target process needs to load a dynamic link library, determining a variable identifier corresponding to a target thread local storage variable needing to be accessed by a patch function and an initial position of an assembly code according to a code snippet associated with the patch function; according to the variable identifier, searching whether target relocation information with the variable identifier exists in the executable file, and if the target relocation information is not searched, modifying an assembly code corresponding to the local storage variable of the same-name thread according to the target offset to obtain a target assembly code; and replacing the initial assembly code with the target assembly code, modifying the relocation type of the local storage variable of the target thread to be relocation forbidding, and updating and loading the dynamic link library. According to the method and the device, the technical problem of patch function loading failure caused by different addressing modes of thread local storage variables in a conventional dynamic mode and a local executable mode is solved.

The invention discloses a conversion method and system based on SysML and Modelica model semantic mapping. The method comprises the following steps that S1, a Modelica standard model library is imported into a SysML modeling tool; s2, completing the definition of behavior elements, and forming an SysML model instance; s3, exporting a Modelica model which can be executed by the Modelica simulator; s4, establishing a mapping relation table between SysML model elements and Modelica model elements; s5, when a change event occurs in the SysML model, updating the content of the corresponding Modelica model; s6, the Modelica model is imported into the SysML model, and a corresponding SysML model instance is generated; s7, simulation result mapping and behavior modeling generation are executed, a state transition diagram structure is formed, and structure updating of internal block diagrams in the system model is completed. According to the method, bidirectional conversion and joint simulation optimization between models are realized, and the method is suitable for cross-platform modeling, collaborative design and functional verification scenes of a complex system.

A network device may receive code for generating an interface for communicating with a large language model, and may generate the interface based on the code. The network device may provide, to the large language model and via the interface, a question associated with the network device, and may receive, from the large language model, an answer to the question associated with the network device.

Methods and systems for generating a sharable script related to an input digital model on a digital platform are provided. The method includes receiving a user request indicative of a digital task involving an input digital model, and retrieving a corresponding input digital model file. Then, determining characteristic attributes of the input digital model, where the characteristic attributes include digital artifacts generated from the input digital model file. Then, selecting from a collection of templates, using a machine learning (ML) engine, a template matching the characteristic attributes of the input digital model. The ML engine may be trained on documentations of digital tools integrated into the digital platform, a resource-capability mapping of the digital platform, and sample digital thread orchestration scripts collected through past uses of the digital platform. Finally, the method includes generating the sharable script that implements the digital task, based on the selected template.

Systems and methods for detecting anomalies in generative outputs are disclosed herein. The system receives a user prompt indicating a request for data over a time period. The system inputs, into a model, the user prompt to cause the model to generate an output based on the user prompt. The system then generates the first tokens based on the output. To generate the second tokens, the system retrieves, based on the user prompt, sources relating to the data requested by the user prompt. The system then generates queries to request, from the sources, the data over the time period and generates the second tokens based on the retrieved data. The system then performs a comparison of the first tokens and the second tokens and accepts or rejects the output of the model based on the comparison.

The invention discloses an AI low-code development component automatic generation method based on a knowledge graph, and relates to the technical field of AI low-code development, and the method comprises the following specific steps: constructing an AI development knowledge graph: constructing a knowledge graph containing a five-layer structure, collecting and preprocessing multi-source data, and extracting various entities and association relationships, so as to obtain an AI development knowledge graph; constructing an ontology structure, importing the ontology structure into a database, and forming a final knowledge graph through manual auditing and machine reasoning optimization; according to the method, the multi-dimensional AI development knowledge graph is constructed, user requirements are analyzed in combination with the optimized natural language processing model, the component configuration scheme is generated by introducing the multi-level reasoning mechanism, and finally the standardized low-code component is automatically generated, so that full-automatic conversion from the user requirements to the low-code component is realized, and the user experience is improved. The defect that an existing low-code platform depends on manual component configuration is overcome, the development period is greatly shortened, and the development cost and the technical threshold are reduced.

The invention belongs to the field of software engineering, and discloses a software development method and system based on a low-code platform, and the method comprises the steps: automatically generating an initialization project through a project demand; designing a workflow through a predefined workflow node and a graphical interface based on the initialization item; synchronizing and recording all changes of the workflow in real time; performing sub-branch experimental development based on the change record, reviewing the sub-branch experimental development, and combining into a new workflow according to a reviewing result; starting the new workflow for testing, and debugging based on a test result to obtain a final workflow; and deploying the final workflow into a production environment, monitoring the running state of the final workflow in real time, and continuously iterating and improving the final workflow based on monitoring data and user feedback. According to the method, all changes of the workflow are synchronized in real time and recorded by performing sub-branch experimental development and examination based on the change records, so that the team cooperation efficiency and the version control capability are improved.

A method, system, computer program product of providing a FiniteStateMachine (FSM) with embedded business rules, processing data & events, where each state is individually saved to blockchain ledger, a data record including a unique identifier / name, a set of input field(s) or input data slots, state rules in a rule language, and set of output field names or output data slots, method including: executing of a Smart Contract by identifying state by id or name, loading corresponding Component State from blockchain, adding / executing code for processing the State's behavior, where the code includes rules for the state in the rule language (e.g., but not limited to. Open Policy Agent, Rego, Prolog, DataLog, RuleML, Drools Rule Language, NRules, and JavaScriptObjectNotation(JSON), etc.). System includes processor(s), application program interface (API(s)) for communicating, blockchain, rule processor, and memory. This enhances transparency, immutability, and secure execution of business rules within FSM architecture.

The invention discloses a webpage generation method and device, electronic equipment and a storage medium, and relates to the technical field of computer software or other related fields, the method comprises the following steps: receiving demand information of a user for an expected webpage, and inputting the demand information into a target language model to obtain N pieces of component information in the expected webpage, N being a positive integer; retrieving a target database based on the N pieces of component information to obtain a retrieval result; inputting fusion demand information of the retrieval result and the demand information into the target language model to obtain a configuration code which is used for describing the page layout, the component attribute and the application logic of the expected webpage; and analyzing the configuration code to obtain a corresponding source code, and rendering the page based on the source code to obtain a target webpage corresponding to the expected webpage. According to the method and the device, the technical problem that the generated webpage does not accord with the intention of the user due to understanding limitation of a low-code platform fused with a large language model in related technologies is solved.