278 results about "Code transformation" patented technology

This disclosure relates to method and system for facilitating legacy code transformation. The method includes receiving legacy code data and natural language document from one or more data sources. Each of the one or more data sources is one of an external data source or an internal data source. Further, the method includes generating a first natural language output based on the legacy code data through a first LLM, and a second natural language output based on the natural language document through a second LLM. Further, the method includes fine-tuning one of the first LLM or the second LLM based on the first natural language output and the second natural language output, through a third LLM. Further, the method includes generating a natural language specification document corresponding to the legacy code data based on the first natural language output and the second natural language output through the third LLM.

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 belongs to the field of artificial intelligence, and relates to a code conversion method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining a source code and a target language; analyzing the source code to generate an abstract syntax tree; analyzing the abstract syntax tree, and extracting semantic information; according to the semantic information and a grammar rule of a target language, converting the code into the target language to obtain a preliminary conversion code; and further optimizing the preliminarily converted code to generate a final translated code. The automation degree is high, and the workload of manual conversion is greatly reduced; the conversion process is standard and unified, and the consistency and accuracy of code conversion are improved; by analyzing the AST, the source code can be understood more deeply, so that the translation code with higher quality is generated; extension and maintenance are easy, and support for new languages or characteristics can be added according to needs; the optimization step ensures that the converted code is not only correct, but also efficient and high in readability; and cross-language programming and code reuse are promoted.

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.

A pre-trained neural code generation model generates repair code for a method containing a performance bug given a prompt including a code transformation instruction. The code transformation instruction guides the model on how to predict the repair code when the model has not been fine-tuned for the repair code task. The code transformation instruction is retrieved from abstract bug patterns derived from historical performance bug fixes found in commits to a source code repository. The augmentation of the code transformation instruction in the prompt to the pre-trained neural code generation model provides the model with a hint on how the repair code may be generated based on similar performance bug fixes.

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

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

The invention discloses an industrial software test case generation method and device based on a large language model, and the method comprises the steps: obtaining a prompt strategy and a knowledge base which is used for storing background description information generated by an industrial software test case; according to the prompt strategy, guiding the large language model to generate a test case according to the knowledge base, and outputting a corresponding first test case; acquiring a system function description model; optimizing the first test case by utilizing a large language model according to a system function description model to generate a second test case; and obtaining test case deployment information, and performing code conversion processing on the second test case by utilizing the large language model according to the test case deployment information and the system function description model to generate a corresponding test case execution file. According to the method, the test case can be automatically generated, the generation efficiency of the test case is improved, the quality of the test case is improved, the test requirement of an industrial software system is met, and the method can be widely applied to the technical field of artificial intelligence.

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 discloses a code conversion system and method, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence, and the code conversion system comprises a project input module which is used for obtaining a code engineering project containing a project file, and the code engineering project is written by a first programming language; the data preprocessing module is used for creating a knowledge base corresponding to a code engineering project and splitting a project file into a plurality of split texts; the function analysis module is used for determining a function list of the code engineering project according to the knowledge base, the split text and the first programming language, and the function list comprises a plurality of functions achieved by the code engineering project and calling relations among the functions; and the code conversion output module is used for generating a target conversion code corresponding to the code engineering project according to the function list and the second programming language, so that reliable conversion of any project-level code of any programming language can be realized, and the code conversion effect is relatively good.

[Problem] To enable a user who creates a control program used in a programmable logic controller to enjoy the benefit of an LLM. [Solution] First information pertaining to an input / output apparatus connected to a PLC, second information pertaining to at least one of a command or a program module for controlling the input / output apparatus, and third information indicating control content implemented in combination with the command or the program module are received in a natural language. On the basis of same, a natural language prompt for generating a text code corresponding to a ladder diagram is generated. The prompt is transmitted to the LLM and an answer sentence is received from the LLM. A text code included in the answer sentence is converted into a ladder diagram. In addition, the prompt includes fourth information that constrains the text code included in the answer sentence to be suitable for the conversion into the ladder diagram.

The invention discloses a work order auditing management method and system based on a large language model and a medium. The method comprises the steps of receiving input and correspondingly matching a work order auditing rule; based on the matched work order auditing rule, analyzing and processing the input through a large language model NLP, and converting the input into a natural language rule; carrying out synonym and antonym conversion on input white words, and carrying out equal rule conversion on converted words based on a corpus word library; performing rule text and code conversion on the converted natural language rule; and carding the processed rules into SQL statements which can be identified by the system according to the fields and the judger rule base. Updating the rule knowledge base; and writing the processed SQL statements which can be identified by the program into a rule knowledge base. According to the method, non-technical personnel can adjust and modify the work order information verification rule by using a natural language by using a large language model technology, so that the intelligent level of a work order system is enhanced, the development cost of the system is reduced, the auditing efficiency of a work order process system is improved, and the method has relatively good practicability.

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.

The invention relates to the technical field of computers, and provides a program previewing method and device.The method comprises the steps that an original code and an update code of a program are determined; based on the difference between each character in the original code and the character at the same position in the update code, generating an editing operation sequence which refers to editing operation executed at the same position in the process of converting the original code into the update code; and sending the editing operation sequence to the terminal, so that the terminal incrementally updates the program preview application page based on the editing operation sequence and displays the updated preview application page. According to the program previewing method and device provided by the invention, the data transmission quantity is greatly reduced, and the previewing application page can quickly reflect the latest state of the code, so that the real-time previewing function of code editing is realized.

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 the technical field of computer science, in particular to an intelligent program code conversion method and system based on a knowledge graph. The method comprises the following steps: acquiring a source code and performing structured analysis to generate an initial feature vector of a node; constructing a semantic topology network based on the initial feature vector of the node, dynamically updating the node feature vector and the edge weight, generating an optimized semantic topology network, and mapping the node feature vector to a target semantic space in combination with a cross-language semantic mapping rule of the knowledge graph to generate a target semantic vector; and based on the basic framework of the target language, constructing an initial code, and recursively filling the code snippets to generate a target code. The problem that a traditional code conversion method lacks universality and deep mining of program semantics is solved. A complex dependency relationship is often difficult to effectively identify and analyze, so that the performance and the maintainability of generated codes are relatively poor; and a dynamic adjustment mechanism is lacked, so that the accuracy and integrity of generated codes are relatively low.

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 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.

The invention discloses a test method, a test device, a test tool, a storage medium and computer equipment. The method comprises the following steps: in response to an instruction of executing a test task of a target application in a target scene, determining a test path of at least one to-be-tested functional component associated with the test task; identifying the type of a network communication mechanism between the to-be-tested functional components; mapping a network communication protocol of the to-be-tested functional component to a code conversion template corresponding to the network communication mechanism category to form a test script code of the to-be-tested functional component; and sequentially running the test script code of the at least one to-be-tested functional component based on the test path to execute the test task. According to the method provided by the invention, the time and workload of developing and maintaining a test tool or an engine are reduced, simulation in a real scene can be realized, state monitoring of the target application in the target scene is facilitated, and the quality of the application is evaluated more comprehensively.

The invention provides a code generation method, device and equipment based on script grammar and a medium, and the method comprises the steps that firstly, a target text in an editing area is acquired, and the grammar of the target text is script grammar; segmenting the target text to generate at least one character string, and constructing a corresponding syntax tree for each character string to form a syntax tree queue; and finally, traversing the syntax tree queue, and converting each syntax tree into a target code according to a preset grammar and code conversion rule. According to the method, more standard and unified C + + enabling judgment logic codes can be quickly and efficiently generated.

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.

The invention provides a cross-language migration method based on mixed experts and code conversion data, which comprises the following steps of: 1, synthesizing the code conversion data based on a data distillation method to obtain a code conversion data set; step 2, constructing a hybrid expert model; 3, training the hybrid expert model constructed in the step 2 by using the code conversion data set obtained in the step 1 to obtain a trained hybrid expert model; and step 4, realizing cross-language migration by using the trained hybrid expert model. According to the invention, the hybrid expert structure can ensure that the English ability is unchanged in the code conversion data training process, so that the cross-language enhancement effect of the code conversion data can be further stimulated; the method can be applied to all open source large models in an unlimited manner, and the English cross-language migration capability can be transferred to any language, so that the multi-language capability of the model is effectively improved.

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 an operator compiling method and device, equipment and a medium, and relates to the technical field of artificial intelligence. The operator compiling method comprises the following steps: obtaining a to-be-compiled operator source code, wherein the operator source code is a code compiled by adopting a field-specific language based on Triton; converting the operator source code into Triton intermediate representation, wherein the Triton intermediate representation is used for representing computational logic of the operator source code; carrying out optimization which does not involve chip information on an operator based on the Triton intermediate representation to obtain an optimized intermediate representation; and compiling the optimized intermediate representation through a Triton compiler corresponding to the target chip to obtain a target code. Open sources can be conveniently realized in partial stages in the operator compiling process, so that the partial compiling process of the operator can be conveniently optimized and expanded based on the open sources.

The invention provides an automatic vulnerability detection method based on an intermediate pseudo code and a graph neural network. The method comprises the following steps: converting an assembly code of a binary program into a pseudo code; identifying a sensitive function; constructing a data dependency graph (PDG), and extracting a data dependency sub-graph by taking the key function as a central node; generating a graph semantic embedding vector by combining a pre-training model CodeT5-small and a graph attention network (GAT), and generating a graph structure embedding vector by using a node2vec algorithm; training is carried out through a double-branch feature analysis model based on a graph convolutional neural network (GCN) and a classification model of a multi-layer perceptron (MLP); and detecting vulnerabilities by using the trained model. According to the method, the sensitive function vulnerability of the binary program can be distinguished under the condition that source codes do not exist, the accuracy and efficiency of automatic vulnerability mining are remarkably improved, the limitation of a traditional automatic vulnerability mining method is effectively solved, and the method has important practical application value.

The present invention provides an apparatus for analyzing vulnerabilities of smart contract code, the apparatus including: a meta-information extraction unit configured to extract meta-information about input code, which is source code of a smart contract; an intermediate representation language conversion unit configured to convert the input code into an intermediate representation language based on the meta-information extracted by the meta-information extraction unit; a code analysis unit configured to detect whether there is vulnerable code or a rule violation case by analyzing the input code converted into the intermediate representation language by the intermediate representation language conversion unit; and a vulnerability detection unit configured to generate a final vulnerability model by detecting a vulnerability based on the vulnerable code or the rule violation case detected by the code analysis unit, and also provides an analysis method using the apparatus.

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 provides a composite QR code verification method and system based on a coding transformation mechanism. The system comprises a code generation system, a QR code generation system and a verification server. During generation, the first code and the second code are generated, the codes are converted, the composite QR code meeting the verification rule can be generated, and the composite QR code can be verified. The composite QR code can be applied to the fields of commodity anti-counterfeiting, process management and the like so as to guarantee the authenticity of the commodity endowed with the composite QR code or the reliability of the process management process.

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.