605 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 relates to the technical field of software engineering, in particular to a source code detection method, which comprises the steps of source code preprocessing, code dependency analysis, semantic understanding and analysis, code quality evaluation, dynamic behavior simulation, intelligent vulnerability detection and the like to realize comprehensive detection of potential vulnerabilities and quality problems in source codes. A support vector machine and a K-means clustering algorithm are combined, known and unknown vulnerabilities can be effectively recognized, in addition, a continuous optimization feedback mechanism is introduced, a mixed vulnerability detection model can be continuously optimized according to repair operation and feedback information of a developer, the detection accuracy is improved, and the coverage range is expanded. The generated comprehensive detection report covers code structures, vulnerability positioning, quality problems and optimization suggestions, supports integration of various output formats and development tool chains, helps developers to efficiently repair problems, and improves code quality and project progress; according to the method, the comprehensiveness, the self-adaptability and the user experience of code detection are effectively 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 rapid construction and reuse method and system for an application framework, and relates to the technical field of application framework construction, and the method comprises the following steps: mapping code snippets to a high-dimensional tensor space, extracting multi-level code features, and constructing a code association graph and a deep semantic network; constructing a hierarchical Bayesian network based on the spatial-temporal feature map, executing variational reasoning and adaptive particle filtering, and generating a reconstruction code; driving a multi-level generation network by using a dual-dimension discriminator, optimizing a code structure and a dependency relationship, and generating a final optimization code through program slicing and incremental synthesis; according to the method, rapid construction and reuse of the application framework are realized, and the code quality and the execution efficiency are improved.

The invention discloses a code printing quality control method and system of a laser vision code printing machine, relates to the technical field of code printing quality control, and aims to solve the problem that the laser vision code printing machine cannot perform code printing optimization according to a code printing process. According to the method, abnormal identification is carried out on the unqualified quality indexes in the code printing monitoring data, specific problems such as laser parameter abnormity, equipment faults and hardware problems can be accurately identified, the accuracy enables subsequent improvement measures to solve the problems in a more targeted manner, and the accuracy of code printing quality detection is improved by formulating clear code printing quality detection standards and targets. According to the method, the standardization and consistency of quality detection work are ensured, the division standard of quality evaluation is clarified, the quality evaluation result is more objective and accurate, the coding efficiency is improved by designing a reasonable path type, a starting point, an ending point and a path sequence, the accuracy and no intersection or overlapping of a coding path are ensured through the path simulation step, and the coding efficiency is improved. And repeated coding and waste are avoided.

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.

The embodiment of the invention discloses an automatic code standardizing method and device, a storage medium and a program product. The method comprises the following steps: obtaining a to-be-standardized code; performing grammar analysis on the to-be-standardized code to generate an abstract syntax tree; traversing the abstract syntax tree to obtain the type of each node; collecting node data according to the type; a plurality of mapping tables are constructed, standard rules in a preset standard rule base and optimization rules in a preset optimization rule base are stored in the multiple mapping tables, and the mapping relation between the standard rules and the optimization rules is established; judging whether the node data is abnormal or not based on the type, a standard rule base and a plurality of mapping tables; if yes, optimizing the abstract syntax tree based on the multiple mapping tables and the abnormal node data; and generating a code meeting the specification based on the optimized abstract syntax tree. According to the method, the code quality, consistency and maintainability are improved by automatically standardizing the code, and meanwhile, the development and maintenance cost is reduced.

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.

A computer-implemented method for code classification includes parsing source code stored on a code carrier to obtain a plurality of code structure characteristics of the source code, and evaluating the code structure characteristics on at least three code structure levels. In some embodiments, the at least three code structure levels include one or more of: a global level, a module level, an intermodule level, a function level, an inter-function level, and an implementation level. The evaluation on each code structure level includes, for each of one or more level-specific conditions, determining whether the code structure characteristics violate the level-specific condition, and representing the violation by a weighted value. The method also includes combining the weighted values for at least some of the violated level-specific conditions over the at least three code structure levels to generate one or more code quality indicator for the source code.

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 provides a code defect detection method and system based on a large model. The method comprises the following steps: generating an enhanced semantic understanding vector corresponding to an initial source code by utilizing a pre-trained large-scale language model in combination with an attention mechanism; identifying the logic error pattern to determine the defect type and position of the initial source code; the reinforcement learning algorithm selects an optimal repair path by simulating a plurality of repair paths and evaluating the effectiveness of each repair path, and generates a repair code snippet through the optimal repair path; replacing a corresponding part in the initial source code with the repair code snippet, and generating a target source code; performing quality scoring on the target source code based on the code quality evaluation model to obtain a quality feedback result; and outputting the target source code with the change annotation and the quality feedback result. According to the method and the device, the speed and the accuracy of finding and solving coding errors in the software development process are improved, and meanwhile, the quality of repaired codes is also ensured.

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 discloses a method and device for detecting code quality, and relates to the technical field of computers. A specific embodiment of the method comprises the following steps: acquiring a code file of which the quality is to be detected, and matching an analyzer according to attribute information of the code file; processing the code file by using a parser to obtain an abstract syntax tree; traversing the abstract syntax tree, and determining the circle complexity of the code file through a strategy of counting and judging the number of nodes; and counting the code line number of the code file, determining a quality analysis result of the code file according to one or more of the circle complexity and the code line number, and triggering an alarm in response to the condition that the quality analysis result is greater than or equal to a preset threshold value. According to the implementation mode, the abstract syntax tree aiming at the code characteristics can be generated, the code quality is systematically evaluated from multiple dimensions, and developers are helped to quickly identify complexity and potential problems in the codes, so that subsequent continuous improvement and optimization of the codes are facilitated.

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 relates to the technical field of image processing, in particular to a code spraying quality detection method of an intelligent code spraying machine. The method comprises the following steps: collecting a code spraying area image, obtaining a binary image through dual histogram equalization and binarization processing, carrying out limit connected domain extraction on the binary image, carrying out simulation reconstruction to generate a code spraying model, carrying out skeleton splitting on the simulation model to obtain a basic skeleton, analyzing the extension coherence of the skeleton, and obtaining a code spraying result. The method comprises the steps of generating stripe continuity quality, performing gap dispersion and uniformity evaluation on a simulation model based on a code spraying skeleton to obtain stripe uniformity quality, deducing readability of a sprayed code according to the stripe continuity and uniformity quality, performing quality grade mapping, and generating a comprehensive quality evaluation report. The code spraying quality detection method is more comprehensive and more reliable.

The invention relates to the field of automation, the field of artificial intelligence and the technical field of code quality management, and provides an automatic online code review method and system. The method comprises the following steps: compiling a self-defined rule description document, and compiling or updating a formulated coding specification document; a webhook request is set in a code warehouse in advance and used for automatically triggering an external service or process when a specific event occurs in the code warehouse; compiling or modifying codes, and manually / automatically submitting the codes to a code warehouse; the code warehouse automatically triggers a pre-configured webhook request, and then an interface of an AI code review system is called for code review; the AI review system pulls the to-be-reviewed code, performs rule matching on the to-be-reviewed code according to preset rule template configuration, detects whether a code violating a rule exists or not, and generates a review report after review is completed; wherein the rule template configuration comprises a rule description document and a coding specification document.

The invention relates to the technical field of video coding, in particular to an intelligent video coding optimization system and method based on computer vision, and the system comprises a computer vision video content analysis module; identifying an object, a behavior and a face in the video; the video classification module based on the neural network is in communication connection with the computer video content analysis module; based on the video feature data, classifying videos by using a deep neural network; the video coding parameter optimization module is in communication connection with the video classification module based on the neural network; adaptively optimizing video coding parameters based on the video classification result and the video feature data; the video coding quality evaluation module is in communication connection with the video coding parameter optimization module; evaluating the video quality of coding based on the optimized coding parameters; and the evaluation result is fed back to the video coding parameter optimization module for further optimizing the coding parameter, so that the understanding depth of the video content is greatly improved.

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 provides a software code optimization method and system based on big data, equipment and a medium, and relates to the technical field of computers. The method comprises the steps that a target code library and corresponding running log data are acquired, and a first data set is obtained; cleaning, converting and aggregating the first data set from the programming language dimension, the framework dimension and the project specification dimension to obtain a second data set; training a machine learning model by using the second data set, so that the machine learning model can identify code quality, performance bottlenecks and potential defects; analyzing a code module written by a user by using the trained machine learning model to obtain a code analysis result; and according to a code analysis result, generating a corresponding code segment from the target code library, and displaying the code segment by using a visual interface. According to the technical scheme, accurate and comprehensive code optimization suggestions can be given.

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 embodiment of the invention provides a streaming media video coding method and device, equipment, a storage medium and a product. According to the technical scheme provided by the embodiment of the invention, the coding target code rate is determined according to the sampling bandwidth and the maximum target code rate of the current coding resolution, and the maximum instantaneous code rate is determined according to the coding target code rate, the maximum target code rate, the sampling bandwidth and the bandwidth fluctuation information; and determining the size of a coding buffer area according to the maximum instantaneous code rate and the preset constraint delay, and carrying out streaming media video coding based on a video buffer area checker according to the coding target code rate, the maximum instantaneous code rate and the size of the coding buffer area, thereby accurately estimating the network condition and the network fluctuation condition. The coding target code rate and the size of the coding buffer area are adaptively adjusted, and while the delay constraint is satisfied, the overall picture quality and the transmission stability of the video stream are effectively improved, and the video coding quality 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 video data transmission, in particular to a multi-screen display method and system for a video picture, and the method comprises the steps: obtaining a video frame image to be displayed in each screen and bandwidth data corresponding to each screen during transmission, determining a motion saliency value of each region in the video frame image, obtaining a video coding quality coefficient of each region in the video frame image in combination with the texture distribution condition in the region, calculating a jump sudden reduction coefficient of each bandwidth data, and obtaining a picture compression degree value of each screen when each video frame image is displayed; and based on the picture compression degree value, optimizing a compression coding parameter of each video frame image in each screen. According to the invention, the multi-screen display stability of the video picture can be improved.