821 results about "Object code" patented technology

The invention provides a cross-frame code migration method and system, electronic equipment and a storage medium, and the code migration method comprises the steps: carrying out the frame-level conversion of a first frame code, and generating a target engineering skeleton adaptive to a second frame protocol; analyzing the construction configuration and the entry file, determining an entry point and a compiling rule of code migration, analyzing the first frame code through an abstract syntax tree, and generating a target code range list needing to be migrated; and according to the target code range list and the dependency item list, applying a preset conversion rule to the first framework code, and generating a second framework code adaptive to a second framework protocol. Through automatic framework-level conversion and dynamic rule adaptation, rapid migration from the first framework code to the second framework code is realized, a multi-terminal code library is unified, the manpower development cost is reduced, synchronous operation of business iteration and migration is supported, and the development efficiency is improved.

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

A system for translating source code in a first programming language to a target language is provided. The system is configured to receive source code for converting to target code; determine an abstract syntax tree from the source code; determine program specifications from the source code; determine a dependency graph from the source code; determine a plurality of chunks based at least in part on the abstract syntax tree, the program specifications, and the dependency graph; determine a plurality of converted chunks based at least in part on the plurality of chunks and a deep learning model, the deep learning model converting the plurality of chunks from the language of the source code to the language of the target code; post-process the plurality of converted chunks to obtain intermediate code; and provide the intermediate code as the target code.

The invention relates to the technical field of data processing, and discloses an intelligent code review method and system based on a large language model, and the method comprises the steps: obtaining a to-be-reviewed code, carrying out the lexical analysis, grammatical analysis and semantic analysis of the to-be-reviewed code, extracting the key information of the code, and obtaining a preprocessed code; multiple features of the code are extracted from the preprocessed code, and target code features are obtained; inputting the target code features into a large language model, and generating a code review result through the large language model; according to the method, complex code semantics and logic problems can be processed, the code review accuracy is improved, and omission and errors of manual review are reduced.

The invention discloses a code reconstruction method, system and device based on function recognition and a medium, and the method specifically comprises the steps: scanning a project file topological structure, and constructing a knowledge graph based on a code import relation and a technology stack feature; performing context enhancement by analyzing the code function description and combining with the knowledge graph to generate a code modification instruction set; based on the code modification instruction set, in combination with data stream sensitivity marking and performance portrait analysis, evaluating the target code to obtain an evaluation result; based on an evaluation result, matching a historical optimal practice mode through a graph neural network, and generating an optimization scheme which retains an original design style; by comparing abstract syntax tree differences between original codes and verified optimization schemes, only functional logic nodes are replaced, and legal code style features are reserved. According to the method, the efficiency and quality of code reconstruction and optimization are improved, and a more convenient, efficient and intelligent code processing tool is provided for software developers.

The invention provides a code generation method and device. Comprising the following steps: in response to a code generation request, performing code demand identification on input prompt information to obtain a structured code demand identification result; the code demand identification result comprises a function demand and first context information; the first context information comprises context description content of the function demand in the code demand identification result; determining second context information based on the function demand and the first context information; the second context information comprises complete environment description content of the function requirement; generating a candidate code based on the function demand and the second context information; and performing code optimization on the candidate code to obtain a target code. According to the method, the target code is generated based on the second context information, and the second context information comprises the complete environment description content of the function requirement, so that the code generation system can generate the code according to the complete context information, and the code generation accuracy is improved.

The invention discloses a reasoning optimization method and device for a code generation large model, equipment and a medium, and relates to the technical field of model reasoning, and the method comprises the steps: in the reasoning process of a code generation task of a target code generation large model, executing a multi-granularity uncertainty quantification step in parallel every time a new Token is generated, obtaining a multi-granularity uncertainty score; constructing a state space vector, and utilizing a preset reinforcement learning strategy network to evaluate the selection probability of a plurality of preset reasoning optimization strategies based on a preset smooth decision mechanism so as to determine a target reasoning optimization strategy; if the strategy is a preset reasoning acceleration strategy, optimization processing is carried out through speculation decoding; if the strategy is a preset exploration optimization strategy, performing optimization processing by using a preset multi-path sampling technology and a preset knowledge enhancement technology; if the strategy is the preset fuzzy processing strategy, taking the plurality of candidate outputs as target reasoning outputs for optimization processing; and evaluating the decision effect according to the reasoning result to optimize the preset reinforcement learning strategy network.

The invention relates to the technical field of computers, and provides an agent generation method and device, a computer program product and electronic equipment. The method comprises the steps of generating an initial code corresponding to code task description information based on a pre-trained code generation model; code inspection is conducted on the initial code, when the inspection result indicates that the initial code has a code fault, the initial code is repaired based on the code fault, and a repaired code is generated; performing code logic error checking on the repair code, generating a repair strategy of the repair code based on the code improvement knowledge graph under the condition that a logic error checking result indicates that the repair code has a logic error, and inputting the repair strategy to the code generation model; enabling the code generation model to improve the repair code based on the repair strategy and the code task description, updating the repair code according to an improvement result, and iteratively executing the process until a preset condition is met, so as to obtain a target code. The code generation accuracy can be improved.

The invention relates to the technical field of artificial intelligence, and provides a code generation method, device and equipment based on an AI agent and a computer readable storage medium. First, initial description information is analyzed through the AI agent, and an initial task element component corresponding to a target code is determined; the target big language model further generates a related question list of the target code according to the initial description information and the initial task element component, and executes interactive inquiry on the target user based on the related question list to obtain supplementary description information of the target code, and then analyzes the supplementary description information through the AI agent to obtain the supplementary description information of the target code. And determining a supplementary task element component corresponding to the target code. And finally, generating a target code based on the initial task element component, the supplementary task element component and the component combinatorial logic combination. Therefore, the code is automatically generated through the AI agent and the target large language model, the code generation efficiency is improved, and the user experience is improved.

Embodiments of the present invention provide a code reconstruction method and system, a computing device, a storage medium and a program product wherein the code reconstruction method comprises: performing code analysis on a to-be-reconstructed initial code to generate a target reconstruction scheme wherein the target reconstruction scheme comprises a code reconstruction plan and a code test plan; the code reconstruction plan comprises a function dependency relationship of the target code; analyzing a function dependency relationship in the code reconstruction plan, and generating at least two code reconstruction tasks corresponding to the initial code; executing the at least two code reconstruction tasks in sequence to obtain each initial code snippet, and iteratively optimizing each initial code snippet through static analysis and dynamic verification until candidate code snippets conforming to a function dependency relationship are generated; and compiling a test case of each candidate code snippet based on the code test plan, and testing each candidate code snippet based on each test case to obtain a plurality of target code snippets conforming to the function dependency relationship. And the coherence and the consistency of the reconstructed codes are ensured.

The invention discloses a graph representation learning and retrieval method and system based on a code warehouse graph structure, and the method comprises the steps: constructing a heterogeneous code graph related to an original code, a newly-added code or a modified code, carrying out the graph representation learning of the heterogeneous code graph, generating a node embedding vector, carrying out the self-supervised learning through an intelligent model, and carrying out the retrieval of the node embedding vector. And outputting the updated node embedding vector. The method comprises the steps of detecting whether newly-added codes or modified codes exist or not in real time, only performing heterogeneous graph modification on the newly-added or modified codes, receiving a query request from a user, generating corresponding code snippets according to the request, checking whether the target code snippets meet grammar specifications or not, and outputting a query result. According to the method, the structural relationship between codes is fully modeled, a structure perception retrieval mode is realized, the method is superior to a traditional text matching scheme, and the code semantic understanding capability is improved; and large-scale code graph online query is supported, and the applicability is wider.

The invention relates to an LLVM-based ST language compiling method and system, and the method comprises the following specific steps: taking an ST source code as input, and decomposing the ST source code into a tag stream token through an application lexical analyzer; constructing an abstract syntax tree AST by using the mark flow token through a syntactic analyzer; filling a global symbol table, and transmitting the global symbol table and the abstract syntax tree to the next stage; performing semantic analysis, including type check, generating an abstract syntax tree with additional semantic information, and transmitting the abstract syntax tree to the next stage; analyzing the abstract syntax tree with the additional semantic information, generating LLVM IR language codes and transmitting the LLVM IR language codes to the next stage; and the LLVM compiler compiles the LLVM IR code after receiving the LLVM IR code, and outputs an X86 target code and a PowerPC target code. According to the method, the transportability and the code generation efficiency of the ST language compiler can be remarkably improved, various target architectures are supported, meanwhile, good expansibility and flexibility are achieved, and complex industrial automation requirements can be met.

The invention discloses a code generation method and device of a front-end component and a computer program product. Relates to the field of front-end development, and comprises the following steps: responding to a dragging instruction of a user, dragging a front-end component to a visual interface, responding to a configuration instruction of the user, configuring attributes, styles and events for the front-end component, and obtaining a configured front-end component, the front-end component comprising at least one of a button, a textbox, a card and a table; determining a first target data model of the configured front-end component, and generating a front-end code of the first target data model based on the component knowledge base and the code generator; responding to a natural language instruction for describing the business logic of the front-end component by the user, and inputting the natural language instruction into the large language model to obtain a front-end logic code of the front-end component; and determining the front-end code and the front-end logic code as target codes of the front-end component. Through the method and the device, the problem of low code development efficiency of the front-end component in related technologies is solved.

The invention discloses a code repository question and answer method based on a hierarchical code knowledge graph, which comprises the following steps: collecting all files of a target code repository, and preprocessing to obtain code files and text files; generating an abstract syntax tree from the code file, and extracting related entities; carrying out block segmentation on the text file, and extracting to obtain text blocks; constructing a code knowledge graph representing a code structure based on the extracted entities and text blocks; generating a hierarchical code knowledge graph by adopting a community detection algorithm and a hierarchical clustering method, and generating a functional abstract for each community in combination with a large language model; a Monte Carlo tree search-based code warehouse question and answer agent is used for performing question and answer path optimization, an optimal query track is dynamically planned through the steps of node selection, expansion, simulation, reward evaluation and the like, and a path with the highest accumulated reward is selected to generate a final question and answer result. The code knowledge modeling quality and the reasoning accuracy and efficiency of the question answering system can be effectively improved.

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 embodiment of the invention provides a code conversion method and device, a storage medium and a program product. In the code conversion method, a code acquisition operation can be responded, an initial code of a target function object written in a first programming language is acquired, and knowledge information required for converting the initial code into a target code written in a second programming language is acquired in a target knowledge base. And calling a code conversion model, and converting the initial code according to the knowledge information to obtain a target code. Wherein the knowledge information at least comprises functional attribute information and / or programming knowledge information of the target functional object, so that the code conversion model can learn or supplement specific domain knowledge required for converting an initial code written in a first programming language into a target code written in a second programming language, and the target code written in the second programming language can be converted by utilizing the specific domain knowledge. The initial code is converted into the target code meeting the requirement, so that the dependence of the code conversion operation on manpower is greatly reduced, and the cost of the code conversion operation is reduced.

The invention provides a software component analysis method oriented to C / C + + source codes, relates to the technical field of software component analysis, and provides a flexible and efficient data collection mechanism, comprehensively constructs a TPL feature library to improve the quality of the feature library, preliminarily filters non-specific TPL functions in combination with a directory structure, reduces noise interference and improves the software component analysis efficiency. The method comprises the following steps: firstly, classifying similar TPLs into the same family by using a clustering algorithm, sharing a certain number of common functions in each family, and meanwhile, including a unique marker function of each TPL, further analyzing a multiplexing relationship between the TPLs sharing the common functions, and removing the common functions which do not belong to a specific TPL range, thereby overcoming the defects related to the birth time of the functions; according to the method, the multiplexing proportion of the target code at the function level and the file level is further comprehensively analyzed, the TPL multiplexing situation of the code is accurately judged, the target code is divided into TPL modules, the dependency relationship between the modules is analyzed through include and extran statements, and therefore more accurate component recognition and dependency analysis are achieved.

The invention discloses a code generation method and device based on a large model, equipment, a medium and a product, and the method comprises the steps: carrying out the code generation through a first code large model according to target code demand data, and obtaining an initial code; according to the first code snippet, generating a test code through a second code large model to obtain a unit test code; according to the unit test code and the initial code, code repair based on editing distance regularization is conducted through a third code large model, and a to-be-analyzed code is obtained; performing iterative optimization on the to-be-analyzed code and the unit test code to obtain a target code corresponding to the target code demand data; according to the method, code generation, unit test generation and code repair are carried out in a multi-model cooperation mode, the reliability and accuracy of code generation can be effectively improved, software security vulnerabilities are reduced, and therefore the stability and security of a software system are improved.

The invention provides an intelligent code review method and system, and relates to the technical field of program testing, and the method comprises the steps: binding a triggering mechanism based on an item needing code review in a code management tool by a developer; submitted local codes are obtained, a background interface is automatically called according to a triggering mechanism, and the local codes comprise incremental codes or full-quantity codes submitted by a developer; if it is determined that the type of the local code is an incremental code, the local code is filtered, and submission information corresponding to a target code meeting a filtering condition is written into a cache to wait for a timed task to execute a review task; when the timed task is executed, the submission information is taken out from the cache, and the target code is split into single submission codes according to the submission information; scoring each submitted code to obtain code scores, and giving review suggestions for the submitted codes; and counting an average score of the plurality of code scores to obtain a comprehensive score corresponding to the target code. The code review efficiency can be improved.

The invention provides a multi-agent-based code conversion system and method. The method comprises the following steps: preprocessing an input source code; performing block processing on the source code to obtain source code blocks, obtaining variable information and array size information of the blocks corresponding to the source code, and integrating the variable information and the array size information into complete variable information; fusing the code description file, the code blocks of the source codes and the variable information of the blocks corresponding to the source codes to generate target code blocks; debugging and checking the target code blocks in combination with the source code blocks; the code description file is used for guiding the intelligent agent to establish a jumping type relation in the code conversion process, and it is ensured that the generated target code better meets the actual function requirement of hardware; a block identifier is inserted into a long code to divide the long code into a plurality of blocks, so that the limitation on long code conversion is relieved; codes of different styles can be output by simply adjusting the cue word of the intelligent agent; through multiple times of debugging verification, the conversion quality of the target code is ensured.

Provided in the present disclosure are a code construction method, a model fine-tuning method, and a device and a storage medium. The code construction method comprises: acquiring requirement information and source code text, which is implemented on the basis of a source programming language and corresponds to the requirement information; on the basis of the requirement information, obtaining at least one piece of corresponding candidate code text by means of a generation model, wherein the at least one piece of candidate code text is implemented on the basis of a target programming language; and checking the at least one piece of candidate code text on the basis of the source code text, so as to obtain target code text, which is implemented on the basis of the target programming language and corresponds to the requirement information. By means of the present disclosure, code text corresponding to a target programming language can be effectively expanded, thereby improving the efficiency and accuracy of the construction of the code text, and then fine-tuning training can be performed on a model on the basis of constructed code text, thereby improving the processing capability of the model for the target programming language, and also improving the training effect of the model.

The embodiment of the invention provides a code error repairing method based on a compiler, which comprises the following steps: receiving a target code written by using a target language, and executing a first repairing operation on the target code in the process of compiling the target code by the compiler to obtain a first repairing sequence, the first repairing operation comprises lexical error repairing corresponding to the lexical analysis process and grammatical error repairing corresponding to the grammatical analysis process. Performing a plurality of rounds of semantic repair operation based on an abstract syntax tree, wherein the abstract syntax tree used for the first round of semantic repair operation is generated after the first repair sequence is subjected to syntactic analysis by the compiler; any round of semantic repair operation comprises the steps of cloning a current abstract syntax tree to obtain a cloned syntax tree, and performing semantic attribute labeling based on the cloned syntax tree; and for any error node with the error attribute label in the clone syntax tree, performing semantic repair on a node corresponding to the abstract syntax tree.

The invention relates to the technical field of computers, in particular to a source code hierarchical positioning method, electronic equipment and a medium. Determining a target hierarchy corresponding to a target code line, a code range corresponding to the target hierarchy and code ranges of all target sub-hierarchies based on the code line hierarchical structure information of the code file; and displaying the code lines belonging to the target hierarchy, the code range corresponding to the target hierarchy and the code range of the target sub-hierarchy in the code lines in the visual range by adopting different display identifiers. According to the invention, code levels concerned by a user can be quickly and accurately positioned.

The invention discloses a software code case generation method and device, a storage medium and electronic equipment, and relates to the technical field of computers.The software code case generation method comprises the steps that testing parameters of a reference case set for software codes are detected, and different reference cases in the reference case set are used for testing different code paths of the software codes; according to the characteristic that different reference cases have different contribution degrees to the test coverage rate of the software code due to the fact that the test coverage rate of the software code is different, candidate cases with the contribution degrees to the test coverage rate larger than a target contribution degree are screened out from a reference case set, and then target code paths tested by the candidate cases are obtained; according to the method, the reference case set is adjusted with the aim of increasing the association degree between the code path tested by the test case in the reference case set and the target code path, the target case set is obtained, the technical problems that the generation efficiency of the test case is low and the like are solved, and the technical effect of improving the generation efficiency of the test case is achieved.

The invention provides a scene-based code generation strategy optimization method, and relates to the technical field of software development, and the method comprises the steps: determining a target code template based on the analysis of project demands; analyzing the coupling degree between different code snippets to generate a reference code generation sequence; combining the reference code generation sequence with the code key factors to dynamically generate a corresponding actual code part; and testing the actual code part and optimizing according to a test result. A target code template is determined on the basis of analysis on project requirements, and an optimized code generation strategy is provided for a specific scene; analyzing the coupling degree between different code snippets to generate a reference code generation sequence; combining the reference code generation sequence with the code key factors to generate a corresponding actual code part; the actual code part is tested and optimized according to the test result, the codes meeting the requirements can be accurately generated according to different project requirements and specific scenes, and the software development efficiency and quality are improved.

The LLM-based code completion method comprises the following steps: splitting a code file stored in a code warehouse into a plurality of code snippets, and calculating relevancy scores, corresponding to the code snippets, of a to-be-completed target code file; the relevance score is used for representing the relevance degree of the code snippets and the target code file; at least one target code snippet with the highest relevancy score is screened out from the multiple code snippets, the at least one target code snippet serves as a query index, and a context code set used for conducting code completion on the target code file is retrieved from a knowledge base; wherein the knowledge base is constructed on the basis of code files stored in a code warehouse; and inputting the retrieved context code set and the target code file into a first LLM used for code completion, so that the first LLM performs reasoning calculation on the target code file based on the context code set, and a completion code corresponding to the target code file is generated.

The invention relates to the technical field of vulnerability detection, in particular to a code-level security vulnerability detection method, electronic equipment and a storage medium. The method comprises the steps of obtaining a logic graph corresponding to a target code, wherein the logic graph comprises a role node, a service entity node, an operation edge and an access constraint edge; obtaining an application scene label of the target code according to the logic graph; inputting the target code into an AST resolver to obtain a syntax tree corresponding to the target code; obtaining a sensitive function library B and a suppression function library C matched with the target code according to the application scene label of the target code; and judging whether the target code has security vulnerabilities or not according to the syntax trees corresponding to the B, the C and the target code. According to the method, the unauthorized risk vulnerabilities in the program codes can be effectively identified.

Systems and methods for efficiently converting program code from a source language to a target language. Input source code is parsed into an intermediate representation (IR) using a processor device. Structural and semantic models of the source code are established by applying static analysis to the IR, and a program architecture of the target code is constructed from the IR, including generating context-aware placeholders. The IR is transformed into a single static assignment (SSA) form, and a system dependency graph (SDG) is constructed from the SSA form. The SDG is traversed to sort the translation tasks, and the sorted tasks are translated into a target language using a large language model (LLM). A translated program is generated by integrating the translated code segment into a coherent program structure in the target language.

Aspects of the present disclosure provide systems, methods, apparatus, and computer-readable storage media that support securing code by dynamically inserting digital signatures in target code and maintaining the digital signatures in a linked structure (e.g., a blockchain-type structure).