149 results about "Code transformation" patented technology

The invention discloses a method for converting a natural language into a low code based on multi-modal reinforcement learning, which comprises the following steps of: performing word segmentation, embedding and multi-layer feature extraction on a natural language instruction input by a user, combining a self-attention mechanism and a graph attention mechanism, extracting and optimizing an original semantic feature vector, and automatically identifying a business field; semantic relationship triples in the domain knowledge graph are fused, and a multi-modal semantic alignment and context enhancement strategy is adopted, so that the accuracy and stability of semantic representation are remarkably improved; when semantic drift or ambiguity is detected, a multi-candidate correction mechanism is triggered to obtain a better analysis result, and the robustness of semantic understanding is enhanced; and finally, mapping an analysis result into an instruction which can be identified by a low-code platform, automatically generating a code structure, continuously optimizing a semantic model and a knowledge graph based on user feedback, realizing adaptive learning, and improving the conversion efficiency and quality from a natural language to codes.

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

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

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

The invention relates to a text-based software source code multistage feature generation method, and belongs to the technical field of software code reuse detection. According to the method, the Hash values of the file and the effective code are calculated through the Hash algorithm, the file fingerprint and the code fingerprint are formed and used for file or code-level code reuse detection, the detection efficiency is high, and the detection result is accurate; effective code hash values are calculated through a hash algorithm, code fingerprints are formed and used for code reuse detection of the code level, the influence of code modification modes such as adding or deleting annotations, spaces, table making characters, car returning and line feed codes on code reuse detection in the code reuse process is eliminated, and the detection rate of code reuse detection is increased; according to the method, the features of the file, the effective code and the code block are calculated through the Hash algorithm, the software source code with the complex content is converted into a set of Hash values with the fixed length, the code reuse detection efficiency is improved, and efficient code reuse detection in a large-scale data scene is achieved.

A black box code search model backdoor attack method based on learnable discrete code transformation comprises the steps that a learnable backdoor generator is constructed, and malicious codes with backdoors are generated on the premise that internal parameters of a damaged model are not accessed through the learnable discrete code transformation. Firstly, an agent model capable of simulating victim model behaviors is trained through query-response data; secondly, on the proxy model, a discrete selection process is differentiable by using a re-parameterization sampling technology, and a backdoor generator is trained in combination with a multi-objective loss function so as to realize effectiveness and concealment of backdoor implantation; according to the method, the limitation of a black box is successfully bypassed by constructing the proxy model, and a new possibility is provided for an attacker. A backdoor generation process is integrated into a differentiable training framework through learnable discrete transformation, so that end-to-end learnability is realized, and an attack strategy can be automatically optimized.

Systems, computer program products, and methods are described herein for generating and mapping unicode for cross-distributed network data transmissions. The present disclosure is configured to identify a cross-distributed network data transmission comprising source ledger code; generate a core schema for a destination ledger, wherein the destination ledger is based on data of the cross-distributed network data transmission; generate, by a ledger transformation module, a data dictionary, wherein the ledger transformation module receives ledger code from a large language model (LLM); generate code mapping instructions for the cross-distributed network data transmission based on the core schema for the destination ledger; convert the source ledger code to a unicode; receive, by a receiver schema mapping module, the unicode and the code mapping instructions; and generate, by the receiver schema mapping module, an intermediary ledger comprising the unicode and based on the code mapping instructions.

Systems and methods obtain a translation request accompanying source code of source file(s) for conversion. Formatting of the source code of the source file(s) is verified and the source code is converted to target code that includes target file(s). The converting (i) maps programming language functions of the source code to corresponding programming language functions of the target code and (ii) utilizes, through customized prompt template(s), one or more large language models (LLMs) hosted by a cloud platform during the code conversion, where a source application that uses the source code includes TIBCO™ software. The target file(s) are distributed.

The invention provides a code conversion method and device, electronic equipment and a storage medium, the method is applied to an intelligent agent running in the electronic equipment, and the method comprises the steps that to-be-converted original code data is acquired, and the original code data follows a first protocol; on the basis of a preset rule and a target knowledge base, a function module area and a variable definition area corresponding to the original code data are generated, and the preset rule is used for limiting basic constraint standards of different variables and motion instructions in a second protocol; a plurality of variable definition cases matched with the second protocol and function module cases corresponding to different motion instructions are stored in the target knowledge base; and generating target code data corresponding to the original code data according to the function module area and the variable definition area. According to the method, the robot native code can be automatically converted into the code conforming to the universal open protocol of the automatic system, manual code conversion is not needed, the conversion efficiency is high, and the integration difficulty and cost of the automatic system can be reduced.

The invention discloses a code conversion method and a related device, and the method comprises the steps: obtaining an input code, carrying out the grammatical analysis of the input code, and generating an abstract syntax tree which comprises a plurality of nodes; coding a code text in the input code to generate a first semantic vector; encoding the structure in the abstract syntax tree to generate a second semantic vector; processing the first semantic vector, and converting the first semantic vector into a second semantic vector; generating a control flow diagram according to the abstract syntax tree, and constructing a data flow diagram; determining a plurality of semantic units according to the control flow diagram and the data flow diagram; obtaining a preset domain knowledge graph, wherein the preset domain knowledge graph comprises a plurality of concept nodes; according to the concept nodes and the semantic units, analyzing the second semantic vector to obtain a target semantic vector, decoding the target semantic vector into a target language, and generating a target code. By adopting the method and the device, code conversion expansibility and accuracy can be improved.

To efficiently prepare a program of high quality corresponding to various coding rules and various using program languages.SOLUTION: By the processing 2, the definition file A2 is used to convert the source code described in the predetermined program language into intermediate codes separated for each component having a meaning. The intermediate codes are converted into post-proofreading intermediate codes matched with the coding convention of the target program language by using a coding convention definition file A3 by processing 3. Further, by the process 4, the post-proofreading intermediate codes are converted into post-proofreading source codes of the target program language by using the source code conversion definition file A4.SELECTED DRAWING: Figure 3

The invention discloses a cross-language interaction malicious behavior detection method based on a high-order function, and relates to the technical field of computer security. The method comprises the following steps: receiving a source code of a certain program, modeling a cross-language interface in the program, and identifying and marking a cross-language calling boundary; different language source codes are converted into universal intermediate representations (IR), the IR is combined, and cross-language calling points are marked; constructing a global cross-language function call graph and a control flow graph, determining a function analysis sequence by inverse topological sorting to perform dependency analysis on each function, and abstracting dependency information in the processes into a function abstract in a high-order function form; and parameter instantiation is carried out when function calling is processed, so that the dependency relationship between the cross-language APIs is obtained. According to the method, efficient analysis and transmission of the cross-language dependency relationship are realized through the high-order function abstract and the dependency matrix, the high overhead of constructing a complex system dependency graph by a traditional method is avoided, and the analysis efficiency and the detection accuracy are improved.

The invention relates to the technical field of data processing, and discloses a resource processing service data sending method and device, equipment, a storage medium and a program product. The method comprises the following steps: adding a service type and a service number of a target resource processing service into a to-be-distributed pool; according to the corresponding relation between the service type and the background system, the corresponding relation between the service type and the system interface and the service type in the pool to be distributed, constructing a data processing record, and adding the data processing record into a data pool of the background system; determining a target data processing record in a data pool of the target background system; and starting the available sending thread to determine the service data of the corresponding target resource processing service, and sending the service data to the target system interface according to the data adaptation requirement of the target system interface, so that the target background system processes the service data. The method can adapt to the newly-added or offline change of a background system, and does not need to carry out large code transformation.

This application discloses code generation methods, model training methods, and related products, applicable to computer technology and AI fields. In this application, the code generation device deploys at least one model or has the ability to invoke at least one model. The device can acquire code to be converted written in a first programming language, perform semantic mapping on the code to be converted to obtain an intermediate representation, and use at least one model and the intermediate representation to obtain target code using a second programming language. This application utilizes at least one model to convert code into another programming language, achieving lexical-level semantic mapping during conversion, ensuring semantic consistency between the generated code and the original language, and enabling efficient and accurate conversion of the code's programming language. This application also provides corresponding model training methods and other related products.

A method for facilitating playing a legacy game is described. The method includes receiving user input during the playing of the legacy game, determining whether one or more code blocks for servicing the user input are cached, and accessing one or more instructions of legacy game code upon determining that the one or more code blocks are not cached. The method also includes compiling the one or more code blocks from the one or more instructions of the legacy game code, caching the one or more code blocks, and executing the one or more code blocks to display a virtual environment.

Various embodiments of the present disclosure provide hallucination mitigation techniques for text-to-code conversions that improves the functionality of a computer in various aspects. The techniques comprise receiving a text-based file that defines a set of standards for a prediction domain; generating, using a machine learning model, a decision tree based on (i) the text-based file and (ii) a decisioning prompt for the text-based file; generating, using the machine learning model, computer programmable code for the set of standards based on the decision tree and a code conversion prompt for the decision tree; and providing the computer programmable code to implement an automated task for the prediction domain.

The embodiment of the application provides a code generation method and system for a SysML model and a medium, in the method, a plurality of target model element data in a target SysML model are acquired, element mapping is carried out based on a preset GCK model mapping rule, target GCK model elements corresponding to the target model element data are determined. Subsequently, according to the target GCK model elements and the preset GCK model mapping rule, a GCK model component corresponding to each of the target GCK model elements is called from a preset GCK model component call library to establish a target GCK model. Finally, the target GCK model is code converted by a preset code conversion tool to obtain a target code, effectively improving the code generation efficiency of the SysML model and the development efficiency of the aircraft system.

The embodiment of the invention discloses an AI model compiling method and device, electronic equipment and a computer readable medium. According to the main technical scheme, the method comprises the steps that codes of an AI model are converted, and SSA intermediate representation of the codes is obtained; staticizing each operation execution logic in the SSA intermediate representation to obtain at least one staticized intermediate representation, and each operation execution logic at least comprises a flow control logic, a tensor calculation logic and a scalar calculation logic; and compiling the at least one static intermediate representation to obtain binary data corresponding to the AI model for NPU execution. According to the AI model compiling mode, scalar calculation and tensor calculation mixed AI codes of a general dynamic shape tensor can be converted into static shape tensor codes so as to be suitable for efficient execution of the NPU, and therefore the execution efficiency of the NPU is improved.

A symmetric automatic compiler system (1) and method for high-performance, hardware-optimized automatic parallelization of program code (3) for execution by a multicore or multiprocessor parallel processing system (2) having multiple processing units (21) that simultaneously process instructions for data in a parallel processing system (2) by executing program code (3). The automatic compiler system (1) converts the sequential source code (31) of program code (3) into parallel processing machine code (32) which includes several instructions that can be executed by multiple processing units (21) of the parallel processing system (2) or that control the operation of multiple processing units (21).

The application discloses a multi-source heterogeneous code conversion method for cross-language development, which comprises the following steps: constructing a code sample set by constructing each code sample and a unit code block separated from the code sample into a format of { 'code' x c , 'code type' y c , 'code description' x d}; then using the code sample set to sequentially perform first training of internal features of the model, second training of consistency of code semantics and third training of consistency of mixed codes; taking embedding mapping functions obtained by the model after the first training and the third training as embedding models, obtaining embedding representation vectors after embedding representation mapping by using the two embedding models; in actual use, inputting an initial prompt word into the two embedding models to obtain retrieval enhancement information; and finally inputting the initial prompt word and the retrieval enhancement information into a large language model after splicing to obtain a code conversion result. The application can improve the accuracy of code conversion.

The application belongs to the field of databases and provides a code conversion method, device, equipment and medium based on statement pair equivalent replacement, which comprises the following steps: obtaining a to-be-converted statement including a first operator and a first logical statement in PLSQL code to be converted, the first operator being used for performing a logical NOT operation, and the logical operation result of the first logical statement being Null; obtaining a target logical operation result of the to-be-converted statement; removing the first operator and converting the first logical statement into a second logical statement with a logical operation result of the target logical operation result; and replacing the second logical statement with the to-be-converted statement to convert the obtained target PLSQL code into target Java code. According to the technical scheme of the embodiment, when the logical operation result of the first logical statement in the to-be-converted statement is Null, the to-be-converted statement can be replaced by the second logical statement in a pair-equivalent manner, so that the operation logic remains unchanged after being converted into a Java statement and the normal operation of the system is ensured.

The application provides a compilation method and device of an operator, equipment and a medium, and relates to the technical field of artificial intelligence. The compilation method of the operator comprises the following steps: obtaining operator source code to be compiled, the operator source code being code written in a domain-specific language based on Triton; converting the operator source code into a Triton dialect, performing hardware-independent optimization, the Triton dialect being a hardware-independent dialect based on an MLIR framework and used to represent the computing logic of the operator source code; converting the Triton dialect into a hardware-specific dialect, the hardware-specific dialect being a hardware-related dialect based on the MLIR framework and used to represent hardware-oriented computing logic; generating source code in a general programming language according to the hardware-specific dialect; and calling an LLVM-based compilation tool corresponding to a target chip to generate target code according to the source code in the general programming language. The operator can be applied to chips with different chip architectures.

The application aims to provide a code language model-based generative knowledge graph construction method and device, comprising the following steps: designing a structure-aware code prompt template, the code prompt template comprising a schema prompt template and a text prompt template; given a sample data pair comprising input text and a known knowledge graph, converting the sample data pair into a code prompt using the code prompt template, and converting test data comprising only test text into a code prompt; inputting the code prompt sample data pair and the test data into a large code language model, generating code encoding triples based on a prompt learning method, converting the code into structure triples, and constructing a knowledge graph based on the structure triples. The method and device perform a relationship triple extraction task in a code generation manner, enhance the pre-training model by explicitly modeling structure information, improve the structure understanding ability of the model, and further improve the performance of relationship triple extraction.

The present disclosure relates to a system and method for providing a flowchart-based user interface to a user and generating instruction codes of a microcontroller unit based on an operation process of the microcontroller unit designed by the user. The system for generating the instruction codes of the microcontroller unit according to an embodiment of the present disclosure includes: a memory configured to store at least one instruction; a component storage; a code conversion logic storage; and a processor, wherein the at least one instruction includes instructions for: generating a first flowchart instance based on at least one flowchart component; further receiving a target component and at least one of a property, and a value of the first flowchart instance to generate an operation process; and converting the operation process into an instruction code including an instruction, a register address, and data based on the code conversion logic.

The present disclosure relates to a code language conversion method, device, electronic equipment, storage medium and computer program product, the conversion method comprising: obtaining a C language source file based on an output log of a C language project; extracting different types of basic conversion units from the C language source file; using a large language model to convert each type of basic conversion unit to obtain a Rust language file corresponding to each type of basic conversion unit. In the present disclosure, unlike the conversion method based on syntax rules, the present disclosure can automatically convert C language code into standardized Rust language code by fully utilizing the powerful processing capability of the large language model on code, realizing large-scale, low-error, controllable and safe human-machine collaborative code conversion, and improving the efficiency and safety of migrating old C projects to standardized modern languages.

The embodiment of the invention provides a code conversion method and device, electronic equipment and a storage medium. The code conversion method comprises the steps that a frame and a frame version of an input code are recognized; converting the input code into a to-be-converted element based on a framework version of the input code; converting the element to be converted into an output code; the output code is the code of the target framework version of the target framework.

The application provides a system and method for parsing PLC intermediate code based on compilation technology and automatically generating data bridge description. The system comprises: a preprocessing module for normalizing the C intermediate code generated by matIEC; a lexical analysis module for converting the code into a Token stream; a syntax analysis module for constructing an abstract syntax tree; an abstract syntax tree processing module for traversing the AST to extract type definitions, fields and memory layouts, and converting them into an internal data model; a code generation module for serializing the data model into a standardized JSON file, including data item name, type, size, offset, memory type and nested sub-items; a version management module for automatically updating the compiler version number and embedding the JSON based on CMake during building. The application realizes full-automatic extraction and standardized output of PLC data structure, eliminates manual parsing, ensures accurate memory layout, supports cross-platform integration and version consistency verification.

The embodiment of the invention provides an AI compiler, and relates to the field of communication. The AI compiler comprises a high-level processing layer which is used for converting a CUDA C Kernel code into an NVVM dialect and converting a non-CUDA C Kernel code into a non-NVVM dialect; the middle-end conversion layer is integrated with a first conversion module and a second conversion module, and is used for converting the NVVM dialect into an RISCV vector dialect or a matrix dialect based on the first conversion module and converting the non-NVVM dialect into an RISCV vector dialect or a matrix dialect based on the second conversion module; and the low-level processing layer is used for converting the RISCV vector dialect or the matrix dialect into a pure machine code file, so that the pure machine code file runs on an RISCV chip. Through the embodiment of the invention, the problem that the executable file translated by an AI compiler in the related technology can only run in a non-open source chip generally is solved, and the effect of enabling the generated executable file to run in an open source chip is further achieved.

One or more situations of the invention provide a code data governance method based on artificial intelligence and a related device. The code data governance method comprises the following steps: obtaining a first code processing request; searching the first code graph based on the first code processing request to obtain a first node set; and processing the first code processing request according to first sub-graphs corresponding to the plurality of first nodes in the first code graph to obtain a data governance result corresponding to the first code processing request. Complex project codes are converted into code graphs for expression, graph search is carried out in the code graphs to obtain nodes strongly related to code requests, data management is carried out on the code requests in combination with sub-graphs formed by the strongly related nodes, and a set of universal and efficient code data management logic is provided for different types of code processing requests. Parts of codes related to the code processing request in the project codes are quickly focused, the code data treatment efficiency is improved, and the code data treatment accuracy is improved.