458 results about "Coding quality" 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.

The invention provides a business module source code generation method and system based on large model business reasoning, and belongs to the technical field of code generation. The business module source code generation method comprises the steps that semantic analysis is conducted on business requirements, and a business association graph with weights is constructed; performing module division, and decomposing a complete task into a plurality of sub-tasks; calling a coordination agent, distributing a corresponding module code generation agent based on a large model for each decomposed subtask, and injecting context information to generate a corresponding module code; wherein the module code generation agent learns a corresponding relationship between tasks and injected context information and codes in advance based on field self-adaptive staged training; and calling the verification agent to perform code quality verification. The method has the beneficial effects that the module division is performed based on the business association graph with the weight, the multi-agent collaborative code generation is constructed by adopting the field-adaptive staged training, and the quality of the generated code is verified, so that the code generation efficiency and quality are improved.

The invention provides a data quality detection and improvement system for a large language model, and relates to the technical field of code quality management. The data analysis and characteristic extraction module is used for extracting structured and semantic characteristics of codes and constructing a code distribution diagram; the multi-dimensional quality detection module comprehensively evaluates the data set quality from three aspects of semantic diversity, structural consistency and error mode; the code optimization and verification module generates an optimization suggestion based on a detection result, and verifies the effectiveness of an optimization scheme through dynamic evaluation and unit testing; the feedback and self-adaptive optimization module is combined with developer feedback and runtime performance test to dynamically adjust a repair strategy, and the complex code repair efficiency is improved through quantum computing auxiliary path search; according to the method, full-process automation from quality detection to optimization verification is achieved, the method is suitable for multi-dependency and multi-module complex code repair tasks, and the quality and efficiency of the code life cycle are improved.

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.

A codec system dynamically selects a codec from multiple available codecs with a highest level of encoding quality at a computing device based at least in part on the available computing resources at a particular computing device. The codec system can continuously monitor encoding performance and if encoding with the selected codec uses too many computing resources, then the codec system can switch to a codec that uses fewer computing resources.

The invention discloses an AI-based low-code automatic development method and system and a medium, mainly relates to the technical field of image processing, and is used for solving the existing problems that the demand conversion efficiency is low, the code quality depends on manpower, and the complex scene support is insufficient. Comprising the steps of extracting a current functional entity corresponding to a code function core feature; based on the current functional entity and the historical functional entity, extracting an associated sub-graph containing the current functional entity and the historical functional entity from a preset knowledge graph corresponding to the demand technical field; dSL grammar corresponding to the demand technical field is obtained, entity nodes related to the associated sub-atlas, inter-node interaction logic rules and code function core features are input into the DSL grammar, and intermediate representation IR is obtained; and according to the intermediate representation IR, the code function core features and the current function entity, a model trained by a large-scale code library is utilized to generate a specific code.

The invention relates to the technical field of computer software engineering and cross-platform development, in particular to a system for assisting in transplanting Android language to ArkTS (ArkTS) of the swan Mongolia, which comprises a code input module for acquiring Java and Kotlin double-language source codes, an analysis module for analyzing codes by using an improved grammar analysis algorithm, and a storage module for storing the codes. The translation module constructs an abstract syntax tree based on an analysis result and generates an attribute description array, and the generation module generates an ArkTS interface code according to the attribute description array; the distributed computing architecture realizes efficient task scheduling and processing, and the post-processing optimization module guarantees the code quality. Through multi-module cooperation, the probability-rule fusion algorithm, the type dynamic mapping and other technologies, code transplantation can be rapidly and accurately completed, the large project transplantation period is shortened from several weeks to several days, the manual correction cost is reduced, the code conversion accuracy and the system stability are improved, and the development of the wide-gap application ecology is powerfully promoted.

The invention provides an automatic network protocol testing method and system based on a large language model. According to the method, through three core technologies of structured protocol specification extraction, hybrid test case generation and retrieval feedback enhanced code generation, full-process automation from a protocol specification document to an executable test code is realized. The method comprises the following steps: firstly, carrying out preprocessing and structured analysis on an RFC document, and extracting protocol field information and state machine description; a complete test case covering normal, abnormal and boundary conditions is automatically generated based on protocol elements; and finally, an initial test code is generated in combination with a test equipment API document retrieval result, and multi-round iterative optimization is performed through running log analysis and an experience knowledge base. Compared with the prior art, according to the scheme, an end-to-end automatic test work chain is achieved, new protocol testing can be adapted without manual protocol modeling, meanwhile, a large language model and expert experience are fused through a feedback mechanism, and the test efficiency and the code quality are remarkably improved.

The invention discloses an intelligent template generation method and device based on static codes and a medium, and relates to the technical field of computers, the method comprises the following steps: obtaining user configuration data, and performing dynamic analysis configuration conversion on the user configuration data to obtain a standardized data model; performing context-aware dynamic template rendering on the standardized data model to determine a preliminary code framework; based on the preliminary code framework, code problem parameters are obtained through incremental code quality detection; according to the preliminary code framework and the code problem parameter, determining a user rule code through code problem repair; and performing template dynamic optimization of federal learning on the user rule code to obtain a template logic adjustment instruction. According to the method, the technical problem that template generation logic cannot be optimized according to user feedback due to the fact that code generation and static analysis cannot be coordinated is solved, and coordination of code generation and static analysis and dynamic optimization of the code template are achieved.

The invention provides a code quality adaptive evaluation and optimization method, system and device and a medium, and belongs to the technical field of computers. The method comprises the following steps: performing redundant information cleaning and standardization processing on an original code; selecting an adaptive parser to convert the processed code into an abstract syntax tree, and optimizing the abstract syntax tree; performing embedding processing on each node of the abstract syntax tree by using a pre-trained deep learning model to generate a node semantic vector, and performing aggregation, feature extraction and vector normalization operation to obtain a semantic vector representing code semantic features; extracting a dependency relationship from the abstract syntax tree to construct a program dependency graph, extracting node features by using a neural network model, and generating a dependency relationship analysis result; based on the semantic vector and the dependency analysis result, identifying a code potential problem by using an optimization algorithm and generating an optimization suggestion; codes are automatically modified according to optimization suggestions, the optimization effect is re-evaluated, and related models and algorithms are iteratively optimized according to feedback information.

The invention provides a Java-to-ArkTS code conversion method and system based on a large model, and belongs to the field of computers.The Java-to-ArkTS code conversion method comprises the steps that S1, a Java project to be converted is comprehensively analyzed, the dependency relationship and semantic information between files are extracted, and a code knowledge graph is constructed; s2, determining a code conversion sequence based on the code knowledge graph, extracting context information and multi-modal semantic information of a to-be-converted code, and completing conversion from Java to TS codes in combination with a multi-agent cooperation mechanism; and S3, comparing and analyzing the TS code with an official ArkTS coding specification, identifying a violation code, and correcting and verifying the violation code through a plurality of intelligent agents to generate an ArkTS code which accords with the ArkTS grammar specification and is consistent in semantics. According to the method, the automatic migration from the Java project to the ArkTS project can be effectively supported, the project migration efficiency and the code quality are improved, and the manual intervention cost is reduced.

The invention particularly relates to a mobile camera management system capable of adaptively adjusting video coding quality. According to the mobile camera management system capable of adaptively adjusting the video coding quality, a network condition monitoring and predicting module is used for monitoring the current network bandwidth and packet loss rate in real time and predicting the network state; the video coding control module is used for dynamically adjusting video coding parameters according to the current network bandwidth and the ROI; and the adaptive adjustment strategy module is responsible for issuing a video coding strategy to the video coding control module according to the data fed back by the network state monitoring and prediction module so as to realize dynamic adjustment of the video coding quality. The mobile camera management system capable of adaptively adjusting the video coding quality can avoid video lagging and frame loss when the network bandwidth is relatively low, is suitable for various mobile devices such as a vehicle-mounted device, an unmanned aerial vehicle and a wearable camera, effectively improves the continuity and availability of video transmission, and realizes stable application in an environment with unstable signals.

The invention belongs to the field of code vulnerability detection and restoration, and discloses a code vulnerability detection and restoration method and system based on a general large model, and the method comprises the steps: employing a multi-channel RAG technology, searching a vulnerability library based on a vector embedding method, and retrieving similar vulnerability entries; analyzing the user code structure features based on the abstract syntax tree to obtain predicted vulnerability information; analyzing user intention and code logic based on knowledge extraction to generate a preliminary repair scheme; and carrying out weighted integration on the similar vulnerability entries, the predicted vulnerability information and the preliminary repair scheme, and inputting the integrated results into the general large model to obtain a final detection result and a repair scheme. According to the method, the multi-channel RAG technology is used, related professional content is preferentially retrieved as auxiliary input, the capacity of a general large model in the aspects of code vulnerability detection and repair is improved, and therefore the code quality is guaranteed.

The invention relates to the technical field of video coding and image processing, and discloses a code rate control method and system based on video image segmentation, and the method comprises the steps: carrying out the pixel-level semantic segmentation of a to-be-coded video frame sequence; extracting a foreground region of interest; calculating a corresponding segmentation uncertainty parameter; establishing semantic mutation parameters of the foreground region of interest; generating a semantic perception weight; executing region-level target code rate redistribution and quantization parameter mapping; and executing partition coding control. In the prior art, code rate control mainly depends on motion intensity or pixel complexity, and especially when a foreground target suddenly appears or disappears in a monitoring scene, a technical problem that a key target is blurred or a background code rate is wasted is easily caused. Due to the fact that the uncertainty modeling and semantic mutation sensing mechanism of the semantic segmentation result is introduced, priority coding of the foreground interest area is achieved under the frame-level code rate constraint condition, and the video coding quality and the code rate utilization efficiency are improved.

The invention is applicable to the technical field of code development, and provides a code modification method based on a CURD scene, which comprises the following steps: acquiring a business demand of code modification, and acquiring a data model required by code modification according to the business demand; based on the data model, obtaining a code file related to a CURD scene during code modification; analyzing a calling relationship among functions related to the CURD scene in the code file, and constructing a function calling graph; on the basis of the service requirements and the data model, service code snippets are generated, the service code snippets are combined and modified to the code file, code modification of the service requirements can be processed, and development efficiency and code quality are further improved.

The invention relates to the field of computer software, and provides an AI-based low-code visual component dynamic generation method and system. The method comprises the following steps: receiving a component demand description input by a user, and converting the component demand description into structured cue word data to obtain an initial cue word sequence; retrieving matched template data and grammatical rule data from a component knowledge base according to the initial cue word sequence, and fusing the template data, the grammatical rule data and the initial cue word sequence to obtain an enhanced cue word sequence; performing semantic analysis and code generation processing on the enhanced cue word sequence through a large language model to obtain a component source code; and performing real-time rendering processing on the component source code through a component rendering engine, and generating an interactive component instance in a preview interface to obtain reusable component resources. According to the method, the code quality consistency is ensured, and the development efficiency is improved.

The invention provides an image coding method and system based on a single-step diffusion model, and the method comprises the steps: coding a potential representation to a decoding end through employing an extremely low bit rate based on a stable diffusion model (SD-Turbo) and a depth potential representation compression model, and obtaining a reconstructed image through single-step denoising; a group of auxiliary encoders and decoders are introduced, rich and entropy-perceived pixel-level original image semantic information is extracted at an encoding end, and decoded image structure information is shared for a single-step denoising process at a decoding end; and the whole model is subjected to end-to-end optimization by adopting code rate and pixel level constraint so as to achieve the optimal subjective coding quality. According to the invention, the technical problems of high decoding complexity and poor reconstruction consistency are solved.

The invention relates to the technical field of software engineering, and discloses a software development code review system and method based on reinforcement learning, and the system comprises a historical code feature collection module, a software development code operation parameter collection module, a code feature matching optimization module, a code feature optimization analysis module, and a code review result output module. The method comprises the following steps: extracting code features in a historical code library according to a time sequence, collecting code operation parameters in a software development process in real time, completing evaluation on software development code operation, performing feature matching optimization on the code operation parameters based on an evaluation result, obtaining an evaluation result of a to-be-evaluated operation code after matching optimization, and performing evaluation on the to-be-evaluated operation code according to the evaluation result. Based on the initial evaluation result and the feature optimization analysis result of the software development code, a code review result is output, early warning information is provided for the client through initial evaluation and matching optimization evaluation, and the code quality and safety in the software development process are improved.

The embodiment of the invention provides a two-dimensional code quality detection method and device, equipment and a storage medium, and relates to the technical field of barcode detection. The method comprises the following steps: acquiring a two-dimensional code grayscale image of an identification area; cutting the two-dimensional code grayscale image to obtain an initial recognition image; performing non-uniform illumination correction, size correction and nonlinear correction on the initial recognition image based on the calibration data, and performing Gaussian filtering processing on the corrected initial recognition image to generate a target recognition image; and performing quality grading processing on the target identification image according to the quality characteristic parameter of the target identification image to determine the quality grade of the two-dimensional code. According to the method provided by the invention, through non-uniform illumination correction, size correction and non-linear correction and in combination with Gaussian filtering processing, the quality of the two-dimensional code recognition image can be improved, the influence of environmental factors on the image quality is reduced, and the problems of high misjudgment rate and poor reliability in a complex scene are solved.

The invention discloses a novel digital hybrid coding method based on a large model. The method comprises the following steps: S1, preprocessing to-be-coded data; s2, performing feature coding on the preprocessed data by adopting a diffusion model driven feature learning method; s3, performing neural network coding on the feature coding data, and dynamically adjusting the coding bit number based on a variable bit rate quantization method; s4, performing adaptive entropy coding on the neural network coding data, and performing probability density transformation based on a normalized flow entropy coding method; s5, discrete transform coding is carried out on entropy coding data, and hierarchical quantization is carried out on different frequency components by adopting a hierarchical sub-block quantization method; and S6, based on the code length distribution and entropy coding parameters of the A3C optimization transformation coding data, extracting a lightweight coding model from the diffusion model by using a knowledge distillation method, and generating final optimization coding data. Through the hybrid coding and dynamic optimization strategy, the data compression efficiency and the coding quality are remarkably improved.

The application provides a business module source code generation method and system based on large model business reasoning, and belongs to the technical field of code generation, which comprises the following steps: performing semantic analysis on business requirements, and constructing a business association graph with weights; performing module division to decompose a complete task into multiple subtasks; calling a coordination agent, assigning a corresponding large model-based module code generation agent to each subtask after decomposition, and injecting context information to generate corresponding module code; wherein the module code generation agent has been pre-trained and learned the corresponding relationship between the task and the injected context information and the code based on domain adaptation in stages; and calling a verification agent to verify the code quality. Advantageous effects: the module division is performed based on the business association graph with weights, the multi-agent collaborative code generation is constructed by adopting the domain adaptation and stage-by-stage training, and the quality of the generated code is verified, thereby improving the code generation efficiency and quality.

The invention discloses an incremental code coverage rate acquisition device based on a Java bytecode dynamic instrumentation technology, and relates to the technical field of software testing and quality control. Comprising a code difference analysis module, a byte code instrumentation module, a coverage rate data acquisition module, a coverage rate data integration module, a coverage rate report generation module and a test trigger and threshold control module, and a syntax tree structure of incremental codes is generated based on a Java syntax analysis tool, so that line level analysis of code differences is realized. According to the method, incremental codes are accurately analyzed through Git and JavaParser, coverage rate data are collected through dynamic instrumentation, a unified report is generated in combination with path analysis, missing scenes are marked in a highlighted mode, and the test efficiency and the code quality control capacity are improved.

The invention provides a region-of-interest protection-oriented power image compression method and system and a code stream transmission method, and relates to the technical field of power image compression. According to the method, the region of interest and the background region are distinguished, the background region is smoothed through bilinear downsampling, spatial redundant information of the background region of the power image is removed, and bit allocation of the background region is effectively reduced, so that lower compressed code streams are consumed under the condition that the fidelity of the region of interest and the same background quality are achieved. Meanwhile, through a content weighted attention module, adaptive bit distribution of different spatial positions is realized, and bits can be reasonably distributed according to the importance difference of image contents, so that the coding quality of the region of interest and the overall compression effect are improved. According to the code stream transmission method provided by the invention, the code stream required by each path of image is forwarded in a staggered and ordered manner, so that transmission bandwidth resources are better utilized.

A computer-implemented method and system standardize code patterns within enterprise software environments. A code modernization system utilizes a Large Language Model (LLM) and a specialized prompt library. The library includes pattern recognition prompts to guide the LLM in identifying specific code patterns within selected software code, potentially using enterprise-specific context. It also includes standardized solution prompts to guide the LLM in generating replacement code conforming to predefined enterprise standards for the identified patterns. The system orchestrates communication, transmitting code and relevant prompts to the LLM and receiving identified patterns and subsequently the generated standardized replacement code. This automated approach facilitates improved code quality, consistency, maintainability, and can support code translation efforts within the enterprise.

The invention discloses a code quality automatic evaluation method and device, a server, a computer readable storage medium and a computer program product. The method comprises the following steps: generating a token sequence of an evaluated target code, converting the token sequence into a word vector, performing semantic correlation extraction on adjacent tokens in the word vector, constructing code representation, converting original code tokens from text information into numerical vector information, and inputting a code representation vector into a feedforward neural network model for regression prediction, so as to obtain the target code. And the code quality is automatically scored. According to the method, code representation is applied to code segment score prediction, grammar and semantic information contained in a code token sequence is fully utilized, hidden representation contained in a code segment is mined and used for predicting the quality score of the code segment, objective and fine-grained accurate one-key scoring can be achieved, and low-cost, high-efficiency and automatic intelligent code evaluation is achieved.

The invention discloses a unit test code generation method, system and device and a medium, which are applied to the technical field of code generation, and comprise the following steps: performing program structure interface analysis and abstract syntax tree analysis on a target engineering code to obtain a structured meta-information set and a source code syntax structure model; generating a structured context feature vector based on the structured meta-information set and the source code grammar structure model, and retrieving a historical test case sample set from a historical test case database, wherein the global line coverage rate weighted similarity of the historical test case sample set and the structured context feature vector meets a matching condition; after Prompt template filling of different test subtasks is performed based on a historical test case sample set and structured context data through an Agent agent, different modular test codes are generated by adopting different test code generation models, and closed-loop verification is performed after the different modular test codes are integrated into unit test codes. Therefore, the code quality, practicability and adaptability are improved.

The invention relates to the technical field of artificial intelligence, and discloses an artificial intelligence-based front-end automatic code generation and optimization system and method.A demand analysis and code generation module analyzes a natural language demand through a natural language processing technology and generates a front-end code; the design draft conversion module analyzes the UI design draft through an image recognition technology and generates a response type code; the compatibility detection and repair module automatically repairs the cross-browser compatibility problem; the code optimization module is used for compressing codes, combining styles and proposing reconstruction suggestions; and the self-learning module dynamically adjusts a generation strategy according to developer behaviors. According to the method, development requirements can be automatically analyzed, a design draft can be converted, front-end codes can be generated and optimized, and intelligent interaction, compatibility detection and performance optimization functions can be provided. Through the tool, the development efficiency, the code quality and the team cooperation ability are greatly improved, multiple challenges in the traditional development process are solved, and the tool has wide application prospects.

The invention relates to the technical field of remote collaborative embedded development, and discloses a remote collaborative embedded development system and method.The remote collaborative embedded development method comprises the steps that developer editing behavior data is received, a multi-dimensional intention vector field is constructed, and intention vector representation is obtained; embedded source codes are read, code structure topological mapping is executed to form code structure topological representation, the embedded source codes and the code structure topological representation are fused to create a unified intention-code space model, conflict entropy distribution is calculated to generate a distribution diagram, a self-organizing optimization method is adopted to generate an optimal code merging scheme, and an intention visualization interface is constructed to form a collaborative environment; the core technical problem of remote collaborative development is effectively solved, the development efficiency and the code quality are improved, the conflict management capability is enhanced, collective intelligence formation is promoted, and new member integration is accelerated.

The invention provides a software bill of material analysis method, which comprises the following steps: S1, selecting a software project to be evaluated, and ensuring that a code library of the software project is complete and accessible; s2, scanning the code library and the dependency relationship by adopting an analysis tool, and generating a software component report; s3, identifying a three-party service used in the software project according to the information obtained by scanning the code library and the dependency relationship; s4, according to all the content of the code library obtained through scanning and in combination with the recognized three-party service, self-research codes are accurately distinguished, and the code self-research rate is calculated; and S5, identifying key elements, structures, defects or security vulnerabilities of the codes, comparing the key elements, the structures, the defects or the security vulnerabilities with known correct results or expected results for analysis, and judging the quality of the self-research codes. According to the method, bill of material analysis can be comprehensively and accurately carried out on software projects, self-research codes are clearly defined, three-party services are effectively identified, and code quality is accurately evaluated.

The invention discloses a code generation method and device based on a large model, a storage medium and computer equipment. The method comprises the steps that a UI prototype draft of a target service is acquired; performing element identification on the UI prototype draft through a picture identification large model, and determining element information in the UI prototype draft; generating a code generation cue word based on the element information and a preset cue word, inputting the code generation cue word into a code generation large model to obtain an intermediate code corresponding to the UI prototype draft, and arranging a target service code of the target service based on the intermediate code. The automatic process from UI design to code generation is achieved, the tedious process of manually writing UI components and debugging interaction logic in traditional development is avoided, repeated work and human errors are reduced, developers can put more energy into core service function development, then development efficiency is improved, code quality is guaranteed, and the development cost is reduced. And rapid and high-quality delivery of the project is promoted.