635 results about "Object code" patented technology

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 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 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 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 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 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 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.

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 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.

The invention discloses a code adoption rate determination method and device, equipment, a storage medium and a product, and relates to the technical field of software development, the code adoption rate determination method comprises the steps that a code line information base comprising a first code is constructed, and a reverse index corresponding to the first code is constructed based on the code line information base, the first code comprises one of the following: an auxiliary code generated by a large language model, and a new code of a target code warehouse within a target time period after the auxiliary code is generated by the large language model; and matching a second code with the first code through the code line information base and the reverse index to determine the code adoption rate corresponding to the auxiliary code. Therefore, the technical problem that the actual contribution of the large language model in the software development process is difficult to quantitatively evaluate can be solved, and the technical effect of determining the actual contribution of the large language model based on the code adoption rate is achieved.

The invention discloses a front-end Service code automatic generation method and system, and relates to the technical field of computers.The method comprises the steps that in response to a code generation demand of a user, the code generation demand is analyzed through a preset demand analysis model, and a demand analysis result is obtained; according to the demand analysis result, matching in a preset code syntax tree library to obtain a target code syntax tree; performing parameter optimization on the target code syntax tree to obtain an optimized target code syntax tree; generating an initial code in combination with the optimized target code syntax tree and YApi interface data; optimizing the initial code through a preset code optimization mechanism to obtain an updated code, and outputting the updated code at a front-end window; according to the method, the demand association graph and the interface calling chain are constructed according to the code generation demand, the highly matched syntax tree is screened out from the code syntax library, the corresponding code is generated, code optimization is performed, and the quality and reliability of the generated code are improved.

The embodiment of the invention provides a modeling method, device and equipment for prompt information, a storage medium and a program product. The modeling method for the prompt information comprises the steps of receiving a task processing request, wherein the task processing request is used for requesting to process a code task related to a target code position in a target code file; in response to the task processing request, analyzing the target code file, and performing prompt modeling on the target code file to obtain prompt information; the prompt information comprises code context information of the target code position and code knowledge information of the target code file; the code knowledge information is used for representing code features of the target code file in at least one dimension; and calling the large model to execute a code task related to the target code position based on the prompt information. By adopting the embodiment of the invention, the code capability of the large model can be better stimulated, and the execution effect of the code task is improved.

The invention discloses a target embedded code generation method and device, electronic equipment and a storage medium, and relates to the technical field of code generation. The target embedded code generation method comprises the steps that a target code generation request is received, and a request description text in the target code generation request is analyzed; the entity objects closely related to the target hardware and the interaction relation of the entity objects can be extracted from the embedded knowledge graph. According to the process, natural language description is effectively converted into understanding of hardware characteristics, and therefore prompt words capable of accurately reflecting the hardware characteristics and requirements are generated. Then the generated target cue word is input into a code generation model, an embedded code matched with the target code generation request can be obtained, complex details related to hardware can be processed, the complex details comprise the interaction relation between entities and specific hardware attributes such as a communication protocol, an interface type and signal parameters, and the embedded code can be obtained. Therefore, the accuracy of the generated embedded code can be improved, and the technical problem that the accuracy of the generated embedded code is low is solved.

Techniques are described herein that are capable of performing AI-based generation of a computer program using compiler-gathered semantic information about target code. A user-generated request that requests information about target code is converted into an AI prompt, which requests that the AI model generate a computer program to determine the information. An AI model is caused to generate the computer program, which comprises configuring the computer program to determine, at runtime of the computer program, the information using semantic information about the target code gathered by a compiler and provided to the computer program by an API, by providing the AI prompt as an input to the AI model. A response to the AI prompt that includes the computer program is received from the AI model. Presentation of a representation of the computer program and / or automatic execution of the computer program against the target code is triggered.

The invention discloses a function call relation-oriented code pre-training and generating method and system. The method comprises the following steps: 1) for each piece of code warehouse data in a code data set, extracting context information from the piece of code warehouse data; the context information is function call information, parameter dependence and a global logic structure; 2) training a code-related deep learning model by using the context information to obtain a code generation model; 3) inputting code demand description or a code to be edited into the code generation model to generate a corresponding target code; wherein for the input code demand description, the code generation model generates a target code meeting the demand according to the code demand description; and for an input to-be-edited code, the code generation model automatically repairs or optimizes the to-be-edited code to obtain a target code. According to the method, logic consistency and availability of generated codes are optimized, and code generation in a complex code scene is remarkably improved.

The invention discloses a redundant code detection method, electronic equipment, a storage medium and a program product, and relates to the technical field of software development. An enhanced abstract syntax tree is generated according to a full analysis result of the metadata information containing the dynamic dependency relationship between the codes, and then a code dependency graph is constructed according to the enhanced abstract syntax tree. Generating a dynamic behavior sequence according to dynamic behavior features extracted in a code operation process, and performing multi-modal feature fusion on the code dependency graph and the dynamic behavior sequence by using a graph neural network and time convolutional network hybrid model to obtain redundancy probabilities corresponding to nodes in the code dependency graph; the technical problems that the omission ratio is too high, the granularity is too coarse and misjudgment is serious are solved, and the technical effects that the detection precision of the redundant codes is improved, the detection accuracy of the redundant codes is improved, and memory occupation is reduced are achieved.

A compiling method and apparatus, a device, a storage medium, and a program product. The compiling method comprises: unrolling an instruction sequence in a loop BB to generate multiple BBs and multiple DAGs corresponding to the multiple BBs, wherein the multiple BBs comprise an instruction sequence unrolled at least three times, and the multiple DAGs are used for indicating a data dependency relationship between instructions in the multiple BBs (810); performing window scheduling on the basis of the multiple BBs, and determining a target scheduling window and a scheduling result corresponding to the target scheduling window, wherein the window scheduling is used for dividing the instruction sequence in the loop BB into two parts on the basis of the scheduling window, such that the instruction sequence of the second part in a current loop and the instruction sequence of the first part in the next loop are synchronously executed, the target scheduling window is a scheduling window of which the execution time satisfies a time condition, and the execution time is the time required for executing instructions in the window and calculated on the basis of the data dependency relationship in the multiple DAGs (820); and on the basis of the scheduling result corresponding to the target scheduling window, performing compiling to obtain a target code of the loop BB in a target program (830).

The embodiment of the invention discloses a visual fine adjustment method and device of an interface, a storage medium and a terminal. Firstly, a source code and a running state preview interface are synchronously obtained, and it is ensured that operation is based on a real rendering state. Then, when the user edits the interface elements, the system accurately positions the visual elements to the corresponding positions in the source codes during operation through a pre-established bidirectional mapping mechanism. Based on the accurate positioning, the system generates a code modification instruction and updates the target code. And finally, generating a new preview by running the updated code in real time, completing an operation feedback closed loop, and realizing a real-time editing preview effect. According to the method, manual modification of complex codes is avoided through the complete process, the modification precision is ensured while the low operation threshold is kept through intuitive visual operation, and the operation simplicity and control precision are effectively balanced.

The invention relates to the technical field of computer science, and discloses a cryptographic logic vulnerability detection method and system based on a large language model. The method comprises the following steps: firstly, constructing a diversified cryptographic vector knowledge base, and organizing and managing through text vectorization; based on cryptographic algorithm standards such as FIPS, performing logic compliance check on the target code; in combination with a small number of examples and thinking chain prompts, guiding a large language model to identify potential logic vulnerabilities; summarizing a compliance check and reasoning process result, and retrieving related knowledge in a knowledge base based on cosine similarity to enhance model judgment; and finally, integrating knowledge enhancement and logic analysis results, and outputting a structured vulnerability detection report. According to the method, a universal cryptographic vulnerability detection framework which does not need to run codes and is suitable for multiple programming languages is constructed, automatic recognition and detection of complex logic defects in cryptographic codes are achieved, and the accuracy and reasoning ability of a large language model in a cryptographic vulnerability detection task are improved.

The invention discloses an integrated circuit design Verilog code generation method and device based on a large language model, equipment and a medium, and relates to the technical field of computers, and the method comprises the steps that semantic analysis is conducted on an input language of an integrated circuit user side through the large language model, and a target constraint set is determined based on an obtained performance index set and through a regular expression; a candidate architecture scheme is generated by utilizing thinking chain technology reasoning, the candidate architecture scheme is predicted by utilizing an XGBoost regression model, and a target architecture scheme is determined based on an index prediction result and the candidate architecture scheme by utilizing a non-dominated sorting genetic algorithm; and determining an initial Verilog code based on the target architecture scheme and a preset code template library, and performing code style conversion on the initial Verilog code based on a code style feature corresponding to the integrated circuit user side to obtain a target Verilog code. And the efficiency and availability of integrated circuit design are improved.

The invention discloses a cross-sample interpolation attack and defense method for a depth code model, and relates to a cross-sample interpolation attack and defense method. The invention aims to solve the problems of low adversarial sample generation efficiency and insufficient diversity in the prior art. The method comprises the following steps of 1, extracting multi-category function code samples from a source code corpus, constructing an initial data set and performing preprocessing; 2, randomly selecting a reference sample and a target sample; step 3, extracting different types of interpolation elements from the reference code sample, and adaptively inserting the interpolation elements into a target code sample to generate candidate adversarial samples; 4, proposing an iterative attack algorithm, dynamically optimizing an interpolation strategy, and constructing an efficient adversarial sample of the misleading model through multiple rounds of structural disturbance and confidence judgment; and 5, injecting the generated adversarial sample into the training set, and carrying out model retraining to enhance robustness. The invention belongs to the technical field of information security and artificial intelligence.

The invention discloses an interactive programming data generation method which is used for the technical field of large language models. The method comprises the following steps: receiving a user demand carrying a target code unit, and obtaining a directed graph formed by resource information, the code unit and a dependency relationship thereof in a running environment; screening related resources based on demands to generate a target resource integration text used for describing resource application conditions, and extracting a sub-graph associated with a target code unit through a directed graph to generate a target code segment; combining historical dialogues, resource integration texts and code segment arrangement cues, and guiding the large language model to output prediction information including codes, interpretations or tool calling instructions. According to the method, by dynamically integrating the environment context and the code dependency relationship, it is ensured that the generated code is matched with the actual working space of the user, multi-round dialogue iterative optimization is supported, and the accuracy and practicability of the intelligent agent in interactive programming are improved.

The invention discloses an error log supervision method, device and system and a storage medium. The error log supervision method comprises the steps of obtaining error log information of a target business system; analyzing the error log information, and determining target analysis information; a code line of a target code is positioned according to the target analysis information, submission information of the code line is obtained, and the submission information comprises a code submission object; adding comment information in the code line, wherein the comment information comprises part or all of the target analysis information; in response to the adding operation of the comment information, a prompt message is sent to the code submission object, and the prompt message comprises part or all of the comment information. Therefore, the corresponding code line can be automatically positioned, the analyzed error log information is added into the comment, and the code submission object can be notified in time, so that the convenience of code maintenance is improved.

The invention belongs to the technical field of software detection and repair, and discloses a reinforcement learning-based patch generation system, which comprises a code analysis module, a code defect detection unit, a defect verification module, a reinforcement learning training unit, a patch generation unit and a test and verification module. The code analysis module is used for extracting a source code from an input code file and generating an abstract syntax tree corresponding to the source code; and the code defect detection unit is connected with the code analysis module and is used for identifying the defect position and type in the target code. According to the scheme, end-to-end processing from defect positioning identification to repair is realized through a closed-loop design of code analysis, defect detection, reinforcement learning repair and test verification, a multi-module collaborative architecture is adopted, the defect detection accuracy is ensured by adopting a multi-mode defect detection and defect verification technology, the false alarm rate is reduced, and the detection efficiency is improved. And generating a high-quality repair patch through an interpretable reinforcement learning framework.

The invention discloses a code conflict detection method and device based on multi-dimensional information, and the method comprises the steps: comparing the similarity of a first identifier and a second identifier corresponding to a multi-dimensional label set, and screening out a first identifier and a second identifier with potential code conflicts from all identifiers; for the first identifier and the second identifier with potential code conflicts, code execution path information, data dependency information and life cycle information of the first identifier and the second identifier are obtained from the target code file and the existing project code file respectively, and context information of the first identifier and context information of the second identifier are obtained; and performing path conflict detection, data dependence conflict detection and life cycle conflict detection on the first identifier and the second identifier by analyzing context information of the first identifier and the second identifier. According to the method, the comprehensiveness and the accuracy of code conflict detection can be improved.

The embodiment of the invention relates to a test code generation method, device and equipment and a readable storage medium, test codes are iteratively generated through an LLM model, coverage rate improvement is automatically verified, a compilation failure rollback mechanism and environment adaptation are combined, automatic closed-loop control of test code generation and coverage rate improvement is achieved, and the test code generation efficiency is improved. And the manual writing and debugging cost is effectively reduced. And the generated test code is subjected to multiple rounds of compilation verification, so that the grammar correctness and the execution feasibility are ensured. The generation direction is continuously optimized through a dynamic feedback mechanism, and the coverage range of unit testing on target codes is systematically improved. And finally, automatically terminating the process when a preset quality index is reached, and outputting an effective test code set which is fully verified.

The invention discloses a program decompilation prevention method and device and a medium, and the method comprises the steps: determining a target code required to be protected in a program source code, carrying out the obfuscation processing of the target code and a mixed factor code of the target code according to the obfuscation logic of the target code, generating an intermediate code, and carrying out the obfuscation processing of the mixed factor code of the target code through an SM4 symmetric encryption algorithm, the method comprises the following steps of: firstly, generating an intermediate code, encrypting the intermediate code to generate an encrypted Class file, then, carrying out hierarchical encryption on a preset loading class to generate a multi-layer encrypted loading class, and finally, packaging the encrypted Class file, the multi-layer encrypted loading class and a basic class to generate an encrypted JAR packet. According to the method, the Class file is encrypted through the code obfuscation technology and the SM4 cryptographic algorithm, layer-by-layer encryption is carried out on the self-defined loading class, the complexity after code obfuscation is greatly improved, and the security of source code software is improved.