1701results about "Creation/generation of source code" patented technology

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.

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.

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.

Systems and methods described herein enable adaptive, threshold-based modification of node maps representing ontologies, knowledge graphs, or code development pipelines using generative artificial intelligence. The disclosed platform can retrieve a node map and generate one or more candidate perturbations that modify nodes or relationships within the node map. The disclosed platform can evaluate the effect of the perturbations by comparing respective outputs against ground-truth data. Perturbations can be automatically determined based on changes in external datasets, compliance policies, or operational requirements. The perturbations can be implemented when a computed perturbation quality value satisfies a threshold quality criterion. As such, the system enables efficient, policy-compliant evolution of relational system architectures in dynamic environments.

The invention provides a code generation and evaluation method and system based on RAG and a multilevel decision tree, and the method comprises the steps: integrating project related design documents, and constructing a knowledge base capable of semantic retrieval through a vectorization technology; associating business demand description with related documents in the knowledge base based on an RAG technology, and performing demand semantic enhancement to generate a technology demand cue word; receiving the technical requirement cue word by adopting a large language model so as to generate a complete code conforming to business logic; constructing a four-level decision tree evaluation system, and sequentially executing code quality scanning, deployability verification, dynamic test verification and demand satisfaction verification through an evaluation assembly line to generate an evaluation result; and generating an optimization suggestion according to the evaluation result so as to trigger an iteration generation process when the code does not pass the verification, thereby solving the problems of disjunction between code generation and business requirements, low verification efficiency and insufficient iteration optimization.

The present application discloses a test code repairing method applied to a code development platform. The method comprises: obtaining a test code corresponding to a source code, and then executing the test code to obtain execution information of the test code, wherein the execution information comprises abnormal stack tracking information output during execution of the test code; on the basis of the execution information, performing fault localization on the test code to obtain location information of a faulty code; on the basis of the location information of the faulty code, extracting context information, wherein the context information comprises at least one of a faulty test case, a fault description, or the faulty content; and inputting a prompt constructed on the basis of the context information into a language model and performing inference, so as to obtain a first repaired code. The method is used to perform fault localization in light of the execution information, extract the context information on the basis of the location information of the faulty code, and, on the basis of the context information, generate accurate and complex repaired code via an advanced language model, thereby adapting to various complex fault scenarios and improving repair efficiency and accuracy.

The invention provides a code agent construction system, a construction method thereof and a code generation method, and relates to the technical field of computers, in particular to the technical field of artificial intelligence such as large models. According to the specific implementation scheme, the code agent construction system comprises an intelligent cooperation engine, a layered template library, a semantic understanding and layered retrieval system, a T-RAG generation and optimization system and a closed loop verification system; wherein the layered template library comprises a project-level template, a function-level template and an API-level template; the semantic understanding and hierarchical retrieval system is configured as follows: analyzing user requirements through a natural language understanding module to generate an intention vector, and sequentially executing project-level, function-level and API-level three-level progressive template retrieval; and the T-RAG generation and optimization system is configured to inject the hierarchical retrieval result into the cue word of the large language model to generate deployable codes containing front-end and rear-end logics.

The embodiment of the invention discloses an automatic function chain generation method and device based on a graph database and a medium, and relates to the technical field of graph databases, and the method comprises the steps: receiving natural language query information triggered by a user, carrying out intention analysis on the natural language query information through a preset language model, determining a structured query condition, and sending the structured query condition to the user; the structured query condition comprises a query intention category, a query entity parameter and logic step disassembly information; according to the structured query condition, mixed retrieval is carried out in a pre-constructed graph database, a candidate API calling path set is determined, the graph database comprises an API knowledge graph and node semantic vector information, and the candidate API calling path set comprises at least one candidate API calling path; and performing multi-dimensional scoring on candidate API call paths in the candidate API call path set, and dynamically generating an executable function chain to determine an API call chain code of the target programming language corresponding to the executable function chain.

The invention belongs to the technical field of artificial intelligence, is applied to the field of financial science and technology and the field of medical health, and discloses a rule engine configuration method, device and equipment based on machine learning and a storage medium. The method comprises the steps that historical calculation data and performance indexes in historical calculation tasks are collected, and a training data set is constructed; a machine learning model is trained, and algorithm parameters of the rule engine are dynamically optimized through the training data set; configuring an expression and a user-defined function of a rule engine, and realizing analysis and execution of scripts through JEXL (JavaScript Exchange Library); establishing a dynamic mapping relationship between the algorithm configuration library and the business variables, mapping algorithm variable names and business variable names, and generating configurable algorithm rules; and analyzing task load and resource requirements based on a pre-trained machine learning model, distributing system resources, and repairing abnormal behaviors in a calculation process. According to the invention, the problems of low processing efficiency, high maintenance cost, unreasonable resource allocation and poor expandability in the prior art are solved.

According to the automatic software development method and device and the computer program, intelligent demand analysis is achieved through a natural language processing technology, dynamic task arrangement is conducted through a finite state automaton, collaborative development is completed by means of a multi-specialized role agent, intelligent verification is implemented based on a template matching mechanism, and the development efficiency is improved. And a complete automatic closed loop from demand to delivery is constructed. According to the scheme, full-process automation is realized, manual intervention is greatly reduced, and the development efficiency is remarkably improved; through division and cooperation of multiple agents, the professionality and decision-making quality of each link are ensured, and the capability limitation of a single agent is overcome; flexible and reliable process control is provided based on state machine management, and development state changes are dynamically adapted; and the standard consistency of the delivery result is ensured by combining template verification. According to the method, the problems of long development period, large quality fluctuation and the like caused by chain splitting, low intelligent level and excessive dependence on manpower of an existing development tool are effectively solved, and a feasible path is provided for comprehensive intelligence of software development.

The invention provides an interface communication method, device, medium and equipment adaptive to a swan-monk system, and the method comprises the steps: deploying an interface adaptation layer adaptive to a universal underlying library in a client of the swan-monk system; the method comprises the following steps: acquiring a callback instruction (including a callback function identifier and parameter data which need to be executed by a client of the swan-mong system) issued by a server on the basis of a service request of the client of the swan-mong system, and determining a corresponding target callback function reference from a method mapping table on the basis of the callback function identifier in the callback instruction; the method mapping table comprises a plurality of registered callback functions, and the registered callback functions form key value pairs through callback function identifiers and callback function references; and based on the parameter data and the reference of the target callback function, initiating cross-language calling of the target function of the general underlying library so as to complete execution of the callback instruction and realize processing of the service request. According to the scheme, cross-language communication of the client of the swan-mong system can be realized, and seamless connection between the swan-mong system and the existing general underlying library is realized.

A system and method for optimizing the deployment of a cloud computing environment is presented. The method includes: inspecting the cloud computing environment; generating a representation of the cloud computing environment based on a result of inspecting the cloud computing environment; determining a resource cost associated with each entity of a plurality of entities represented in the generated representation of the cloud computing environment; generating an optimization action for an entity of the plurality of entities, wherein the optimization action reduces the determined resource cost; and initiating the optimization action in the cloud computing environment.

The invention provides a code retrieval method and device for AI programming and related equipment, and the method comprises the steps: constructing a double-layer embedded vector model which comprises a basic semantic coding layer for parameter freezing and a plurality of independent lightweight adaptation layers which coexist; monitoring implicit feedback data generated by interaction of a user in the target code library to extract positive and negative sample pairs, and mining difficult negative samples based on a current model to form a training sample set; when the cumulative number of the samples reaches a preset trigger threshold value, optimizing and updating at least one lightweight adaptation layer parameter by utilizing a preset comparison loss function, and applying elastic weight constraint to key parameters of the lightweight adaptation layer parameter to obtain an updated double-layer embedded vector model; and performing multi-dimensional performance evaluation on the updated model, and when an evaluation result meets a preset publishing condition, deploying the model to a code retrieval service. The problems that in the prior art, cross-domain is difficult, the adaptation cost is high, and catastrophic forgetting is likely to happen are effectively solved, and zero-labeling self-evolution of the model is achieved.

The invention discloses a cross-language code semantic alignment method, which constructs a shared semantic space through a unified abstract syntax tree (AST) and a graph matching network (GMN) so as to reduce the difference of different programming languages in syntax structure and node representation. The method comprises the following steps: (1) mapping a multi-language AST node to a unified general label set and performing structure enhancement; (2) performing node feature coding on the unified AST, and realizing cross-language interaction in combination with a cross-graph attention mechanism; (3) node representation is generated through intra-graph loop updating, and an overall semantic vector is obtained through global attention pooling; and (4) through comparative learning training in the shared space, the distance between semantically equivalent positive sample vectors is shortened, and the distance between non-equivalent negative sample vectors is shortened, so that the discrimination capability of cross-language semantic representation is enhanced. According to the method, the semantic consistency of the functional level can be effectively captured, and the accuracy and efficiency of cross-language code understanding, multiplexing and retrieval are remarkably improved.

Systems and methods include input of a code generation prompt to a text generation model, reception of code from the text generation model in response to the input code generation prompt, execution of the received code, determination of execution information associated with the execution of the received code, input of a repair prompt, the code generation prompt and the execution information to the text generation model, and reception of an updated code generation prompt from the text generation model in response to the input repair prompt, code generation prompt and execution information.

The invention relates to the technical field of data acquisition, in particular to an AOP-based user behavior data acquisition method. The method comprises the following steps: firstly, identifying user behavior related code features by statically analyzing Java source codes of an application program, and constructing a feature template library; during a compiling period, according to a template library identification target method, embedding a point burying instruction by utilizing a byte code enhancement technology, and constructing a monitoring section framework; and during running, dynamically adjusting an execution strategy according to the running state of the application program by virtue of a dynamic rule engine. Meanwhile, an annular buffer area is adopted to asynchronously process user behavior data streams, cache data are managed in a sub-generation mode, and asynchronous processing is triggered when a threshold value is exceeded. The method also constructs an adaptive thread pool model based on a hierarchical thread pool architecture, distributes tasks according to data value weights, monitors the task backlog rate of a high-priority sub-pool, adjusts the sampling frequency or acquisition dimension when the task backlog rate exceeds a threshold value, forms adaptive closed-loop control, and effectively acquires and processes user behavior data.

A display improvement support method and device for improving a display in accordance with the user's conscious reaction is provided. [Solution] In the display object improvement support device, the generation AI control unit executes the following steps: a reaction prediction prompt generation step of generating a reaction prediction prompt that instructs the generation AI to predict the reaction that will occur in the mind of a virtual user having user profile information of at least one of age, sex, occupation, height, and weight when the virtual user uses the display object; a reaction acquisition step of inputting the reaction prediction prompt into the generation AI and acquiring reaction information, which is information related to the reaction, from the generation AI; a screen operation prediction prompt generation step of generating a screen operation prediction prompt that instructs the generation AI to predict the screen operations that the user will perform on the screen of an information device based on the reaction information; and a screen operation acquisition step of inputting the screen operation prediction prompt into the generation AI and acquiring screen operation information, which is information related to screen operations, from the generation AI.

The invention relates to the technical field of software collaborative research and development assistance, and discloses a software collaborative research and development assistance method and device, electronic equipment and a storage medium. The method comprises the steps that instruction information input by a user is analyzed to obtain intention information, and then a corresponding demand sub-agent, a corresponding development sub-agent and a corresponding test sub-agent are determined; performing retrieval analysis on the intention information to compare the intention information with conflict detection of a preset historical demand library to obtain a demand document, executing code generation based on the demand document to obtain an intermediate software code, inputting the intermediate software code into the test sub-agent for testing to obtain a test result, and adjusting the intermediate software code to obtain the test result. And obtaining the target software code. The method has the beneficial effects that the software research and development process is converted from manual cooperation to intelligent execution, and the research and development efficiency is greatly improved while the quality is guaranteed.

The invention discloses a functional programming intelligent recommendation method and system based on a semantic knowledge graph, relates to the technical field of data processing, and constructs a high-dimensional semantic embedding space by performing semantic feature vector extraction on a multi-source functional programming corpus. The method comprises the following steps: firstly, embedding a plurality of candidate entity clusters into a semantic embedding space, preliminarily clustering the embedded vectors by utilizing a semantic aggregation pre-screening mechanism to obtain each candidate entity cluster and performing dynamic processing, then constructing a semantic relationship among the candidate entity clusters according to neighborhood distribution of the semantic embedding space, performing self-adaptive correction on inter-cluster distribution, and constructing a semantic knowledge graph generation recommendation method. A closed loop is formed from the corpus to the knowledge graph to the recommendation strategy, so that the intelligent recommendation system can accurately recognize the function semantic relationship, reduce noise interference and improve the recommendation hit rate and the personalized matching effect, and the accuracy, stability and user satisfaction of functional programming intelligent recommendation based on the semantic knowledge graph are remarkably enhanced.

Efficient generation of multi-platform compatible source code and test code with consistency and quality in software development SOLUTION: The schema-driven development system includes a user input unit, a schema generation unit, and a code generation unit, wherein the user input unit acquires data in various formats such as a natural language and a CSV file, the schema generation unit converts the acquired data into a schema, and the code generation unit generates a source code and a test code based on the generated schema.SELECTED DRAWING: Figure 1

The invention discloses a kernel probe generation method and device, computer equipment and a storage medium, and relates to the technical field of operating system kernel monitoring, and the kernel probe generation method comprises the steps of obtaining a target metadata file corresponding to a current operating system kernel in response to a kernel probe generation request; determining a target declarative strategy file; and determining a target program logic file corresponding to the target declarative strategy file, and generating a probe code according to the target program logic file and the target metadata file to obtain a kernel probe program. The probe program source code adaptive to the current kernel version can be dynamically generated in combination with the target metadata file and the target declarative strategy file, kernel probe development logic and underlying kernel knowledge can be decoupled through the source code generation function, and developers do not need to deeply understand kernel internal data structures and version differences; the development threshold is lower, a specific operating system or probe type is supported, and a universal Linux kernel and multiple probe types are also supported.

The invention provides an action chain dynamic arrangement and verification method based on metadata, and relates to the technical field of computer software, and the method comprises the steps: constructing an action metadata model which comprises an action identifier, a name, a type, an input parameter, a front dependency relationship and a rear dependency relationship; analyzing the action metadata model based on an action chain arrangement engine to identify and obtain business logic, dynamically constructing an action chain according to the business logic, configuring variable parameters of the action chain through an expression, and performing condition verification of the action chain by adopting a preposed verification mechanism; and the action chain executes corresponding business logic on the input parameters according to the sequence, and outputs an obtained output result to the next action.

The invention relates to the technical field of computer software development and databases, in particular to an embedded database programming method which comprises the steps of intention model construction: scanning source codes to identify embedded syntax blocks, constructing an abstract syntax tree, stripping a bottom driver and generating a driver-independent intention model; a logic sequence synthesis step: injecting a dialect parameter and a resource strategy, synthesizing and executing a logic sequence and generating a target code; a fingerprint verification step: intercepting a request during operation, calculating a structure fingerprint, and performing consistency verification on the structure fingerprint and the reference fingerprint; and a binding execution step: executing the request after the verification is passed, and establishing weak reference life cycle binding of the resource object and the host variable. The resource leakage risk is eliminated, and the robustness of the system under long-time operation is enhanced.

The invention discloses a multi-language code generation method based on self-supervised pre-training, which comprises the following steps: acquiring and cleaning multi-language code data to form a training corpus; the method comprises the following steps: representing code data as an abstract syntax tree, extracting a control flow diagram and a data flow diagram of the code data, and obtaining unified semantic representation through combination of a diagram encoder and a sequence encoder; designing a self-supervised pre-training task, and pre-training the semantic representation based on the training corpus; constructing a multi-language pre-training model based on the structure-improved recurrent neural tensor network and the multi-language embedding matrix; when a user inputs a natural language, generating a target language code by using the multi-language pre-training model; and target language code correction is carried out through conventional function testing and grammar checking. According to the method, multi-channel recursive combination and a hierarchical recursive expansion mechanism are combined with self-supervised pre-training, so that accurate generation and performability improvement of cross-language codes are realized.

The invention relates to the technical field of macro text processing, in particular to a non-macro identifier processing method in a macro reference row, electronic equipment and a medium, the method comprises the following steps: S1, obtaining storage information corresponding to each macro reference of the macro reference row, the storage information corresponding to the macro reference comprises a macro reference identifier, a line number in a macro expanded text of a macro reference line and an intra-macro identifier list corresponding to the line number in the macro expanded text of the macro reference line; step S2, obtaining non-intra-macro identifier positioning information and a corresponding to-be-added macro reference identifier in a macro expanded text of the macro reference line; and S3, adding the non-intra-macro identifier positioning information to an intra-macro identifier list corresponding to the corresponding to-be-added macro reference identifier. According to the method, the non-intra-macro identifier binding object after row expansion can be accurately and quickly referenced for the macro, and a debugging function is realized based on the binding object.

The invention discloses a multi-modal sensor abstract description and packaging method and system based on software definition. Firstly, a unified sensor abstract description model based on software definition is introduced, so that hardware independence is realized, and the differences of a bottom-layer multi-mode sensor in the aspects of interfaces, protocols and data formats can be effectively shielded; secondly, abstract packaging of data is achieved through an autonomous information processing module, and high-bandwidth and high-redundancy original data is converted into low-bandwidth and high-value structured information; in addition, by means of automatic registration, discovery, scheduling and control mechanisms of the sensor management control module on sensor resources, the system realizes a real plug-and-play function. According to the invention, by deploying the autonomous information processing modules at various sensor nodes, the original sensing data is preprocessed, and standardized data packaging with semantic consistency is output, so that the operation load of the central controller is reduced, and the information processing efficiency and the system response capability are also improved.

The invention discloses a data processing device, a data processing method, a chip and electronic equipment, and relates to the technical field of data processing. The data processing apparatus includes: a storage module configured to store data; the calculation module is coupled to the storage module and is configured to access an original data vector stored in the storage module, and the original data vector comprises m data elements; splitting the original data vector based on the target k value to obtain a plurality of first data vectors; performing odd-even merging sorting on the data elements in the first data vectors to obtain a plurality of first ordered data vectors; and merging and sorting the data elements in the plurality of first ordered data vectors to obtain top-k data elements in the m data elements. According to the data processing device, the data processing method, the chip and the electronic equipment provided by the invention, the top-k data elements can be obtained without a special hardware sorting network.

The invention discloses a multi-language PLC configuration software component library expansion method and system based on LLVM, and relates to the technical field of configuration software, the method comprises the following steps: obtaining language source codes of various types of programming languages supporting the LLVM front end; inputting the language source code of each type of programming language into a corresponding LLVM front-end compiler, and converting the language source code into a standard intermediate representation; establishing a general language characteristic analysis framework; establishing a general abstract metadata injection framework; building a mapping rule framework; integrating the general language framework, the general abstract metadata injection framework, the mapping rule framework and the output result, and generating a unified extended intermediate representation; and generating an interface component library conforming to the IEC standard. According to the method, any programming language with an LLVM front end is supported, a developer can directly utilize a mature algorithm library in each language ecosystem to select the programming language most suitable for a specific application scene, and the development period is remarkably shortened.

A method of generating a code output in response to a natural language problem description. The method includes: receiving the natural language problem description; generating, by a neural network based language model, a first candidate code snippet based on a first input prompt combining the natural language problem description and a first instruction; executing, at a code execution environment, the first candidate code snippet based on a unit test thereby producing a first feedback reflecting a correctness of the first candidate code snippet; generating, by the neural network based language model, a second candidate code snippet based on a second input prompt combining the natural language problem description, the first candidate code snippet, and the first feedback; and executing, at the code execution environment, the second candidate code snippet based on a runtime test thereby producing a second feedback reflecting a runtime efficiency of the second candidate code snippet.

The invention discloses a spray combustion sub-model integrated development method and equipment based on a large language model, and belongs to the technical field of engines. The method is realized by relying on a retrieval enhancement generation system and a multi-agent collaborative framework, and comprises the following steps: S1, user instruction and thesis extraction: extracting core information from related technical literatures to form a structured file according to a sub-module extension requirement proposed by a user; s2, source code analysis and comparison data generation; s3, function and interface planning: performing adaptive planning of an original model-OpenFOAM platform; s4, code generation and packaging: code conversion, dependency processing, test example generation and compilation packaging; s5, layered testing and verification graph generation; and S6, debugging and repairing: executing a positioning-repairing-regression closed-loop process through a debugging agent. According to the method, the problems that the spray combustion sub-model is difficult to migrate to the OpenFOAM simulation platform under different frameworks and the development period is long can be solved.