292results about "Source to source" 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 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 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.

Apparatuses, systems, and techniques to detect potential security risks using application program interface (API) scanners. In at least one embodiment, software is scanned to identify one or more APIs of different categories and the position of the APIs and associated code in an executable file may be altered to reduce one or more potential risks.

The invention discloses a code conversion method and device, a page display method and device, a medium and equipment. The method comprises the steps that code conversion demand information is acquired, and cue words are constructed based on the code conversion demand information; inputting the cue word into a code generation model to enable the code generation model to generate a source code file of the initial conversion tool; performing transcoding processing on the test case through the initial conversion tool, and deploying a converted code to the environment of the target platform to run to obtain a code running result; constructing a repair prompt word based on the code operation result, and inputting the repair prompt word into a code generation model, so that the code generation model corrects a source code file of the initial conversion tool; a code conversion tool is obtained through testing and correction of multiple iterations; and transcoding the project source code file of the source code platform through the code conversion tool to obtain a code file of the target platform. According to the method, the cross-platform code conversion efficiency can be improved.

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 discloses a spray combustion sub-model integrated development method and equipment based on a large language model, and belongs to the technical field of engines. The method is realized by relying on a retrieval enhancement generation system and a multi-agent collaborative framework, and comprises the following steps: S1, user instruction and thesis extraction: extracting core information from related technical literatures to form a structured file according to a sub-module extension requirement proposed by a user; s2, source code analysis and comparison data generation; s3, function and interface planning: performing adaptive planning of an original model-OpenFOAM platform; s4, code generation and packaging: code conversion, dependency processing, test example generation and compilation packaging; s5, layered testing and verification graph generation; and S6, debugging and repairing: executing a positioning-repairing-regression closed-loop process through a debugging agent. According to the method, the problems that the spray combustion sub-model is difficult to migrate to the OpenFOAM simulation platform under different frameworks and the development period is long can be solved.

The present disclosure relates to a computer-implemented method for, in particular automatically, transforming, in particular preparing and / or migrating, a legacy source code associated with at least one legacy system, in particular a legacy software system, comprising: adapting, in particular training, an artificial intelligence (K1) initial model that is trained, in particular pre-trained, on a programming language of the legacy source code using training data associated with the legacy source code to generate a legacy source code transformation model, and processing at least a portion of the legacy source code using the legacy source code transformation model to generate a, in particular at least partially, transformed source code.

In an example embodiment, a framework is provided to allow an LLM to regenerate an intermediate representation based on natural language feedback that is provided on the compilable computer code that was generated using the programmatic component from an earlier intermediate representation. The user is able to provide this feedback without having any knowledge of the intermediate representation and the framework is designed to allow the LLM to incorporate this feedback into future generation requests without the LLM being aware of the compilable computer code that was created from its prior iterations.

A system may support adaptive user interfaces (UIs) for computer-aided technology (CAx) applications. The system may include a CAx tracking engine configured to track command usage of a computer-aided technology (CAx) application to obtain command usage data for the CAx application and a CAx training engine configured to train a machine learning model with the command usage data. The system may also include a CAx adaptive UI engine configured to obtain real-time command usage by a user of the CAx application; and apply the machine learning model to adaptively transform a UI of the CAx application, including by inputting the real-time command usage of the CAx application to the machine learning model to determine a predicted command based on the real-time command usage and dynamically updating a selected sub-section of the UI of the CAx application to present the predicted command to the user.

The invention discloses a Direct3D12 sampler addressing mode compatible method based on double buffer preprocessing, which comprises the following steps of: establishing a preprocessing parameter cache pool, a first association table, a second association table and a resource management linked list in a system initialization and starting stage, and registering a Vulkan expansion structure body for expanding a texture coordinate range and marking MSAA sampling point offset; when the textures are created, the VKD3D judges whether the textures are MIRROONCE related textures or not, allocates resource marks to the related textures, sets dependency marks, allocates double buffers and expands a coordinate range, and executes standard conversion if the textures are not related; when a sampler is created, marking an analog sampler, constructing and configuring a Vulkan sampler structural body, and updating an association table and a resource management linked list; when a pipeline is created, the D3D12 shader is converted into SPIR-V to update the second association table; and when the recording command list is applied, generating a command buffer, and submitting the command buffer to complete rendering.

Systems and methods are described for processing contract documents. In an example, an application can identify text in an image-based data file that contains text of a contract. The application can segment text in the document into smaller units. The application can then create vector embeddings for each unit and feed the vector embeddings into a first large language model (“LLM”) that classifies each unit. Based on the classification, the application can retrieve a prompt template for each unit and feed the vector embeddings with their corresponding prompt into a second LLM. The second LLM can extract specific data from each unit based on the prompt template. The output from the second LLM can then be converted into a useable format, such as a web page or passed to another system.

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.

Certain aspects of the present disclosure provide techniques for generating knowledge graphs from program source code. An example method generally includes receiving a source code definition of a workflow implemented in an application. The source code definition of the workflow is converted into an intermediate representation of the workflow, the intermediate representation comprising a condensed version of the source code definition. An abstract syntax tree representation of the workflow is generated based on the intermediate representation of the workflow. A structured file is generated by traversing the abstract syntax tree representation of the workflow, wherein the structured file comprises a definition of the workflow in a knowledge graph-specific language. The structured file is deployed to a knowledge graph execution engine.

One or more unit-test cases are generated from a monolingual code corpus and the generated unit-test cases are filtered to generate a corpus of unit-test cases which have acceptability scores exceeding one or more predefined thresholds. One or more of the code samples of the monolingual code corpus are translated from a source language to a target language using a pretrained Large Language Model and the generated unit-test cases are translated from the source language to the target language. The LLM-translated code samples are validated using the translated unit-test cases and a parallel-data training corpus comprising the LLM-translated code samples that pass the validation is created. The pretrained large language model (LLM) is fine-tuned using the parallel-data training corpus, a given code segment is translated using the fine-tuned large language model (LLM), the translated given code segment is tested and the tested given code segment is deployed.

Metadata associated with program code documentation is identified, wherein the program code documentation is associated with corresponding program code. A natural language question regarding the corresponding program code is obtained via a virtual agent. A response to the natural language question is determined based on the metadata using one or more trained machine learning models. The response to the natural language question is provided to the virtual agent.

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.

Provided are systems, methods, and apparatuses for systems and methods of programming processes in processing in memory (PIM) systems. In one or more examples, the systems, devices, and methods include enabling, via a processor, a PIM manager for processing of PIM instructions, the PIM manager being located on a base die of a stacked memory module; enabling, via the PIM manager, a plurality of processing units of one or more memory dies of the stacked memory module for processing; compiling source code comprising the PIM instructions into machine code; loading, via PIM manager, the machine code into an allocation of memory of the stacked memory module; and executing, via the plurality of processing units, the PIM instructions based on loading the machine code into the memory of the stacked memory module.

The invention provides a method and a device for integrating G codes of an integrated IEC (International Electrotechnical Commission) 61131-3 programming system. The method provided by the invention comprises the following steps: in an expanded IEC (International Electrotechnical Commission) 61131-3 programming environment, compiling a mixed program containing a G code and an IEC 61131-3 standard language code at the same time; performing standardization processing on the G code to generate a structured G code abstract syntax tree; mapping and converting the G code abstract syntax tree into a function block call and logic statement meeting the IEC 61131-3 standard; the converted content is fused with an IEC 61131-3 standard language code, and an executable code is generated; executing the executable code according to a preset task scheduling rule, and collecting hardware execution data as feedback information in real time; and performing joint debugging on the G code and the IEC 61131-3 standard language code based on the feedback information to obtain a hybrid control program suitable for an industrial control scene.

Technologies for code-to-code translation and program synthesis are disclosed. An example method includes analyzing input source code to generate dependency graphs corresponding to the input source code, creating a set of code generation tasks for generating target code based on the dependency graphs, and feeding the set of code generation tasks to a trained large language model (LLM) to generate one or more parts of the target code.

This application discloses code processing methods, apparatuses, and storage media. An example method includes: obtaining a first code in low-level language and applicable to a source platform; decompiling the obtained first code to obtain an intermediate representation (IR); and then compiling the IR into a second code in low-level language and applicable to a first target platform, where the source platform and the target platform have different instruction sets.

Modifications to existing computer hardware, compiler changes or source-to-source transforms performed during the software build process, and a collection of libraries and modifications to existing standard system software and libraries. The invention allows a program author to enforce various kinds of locality of causality in software to provide enforcement of boundaries for the following aspects of a computer program: control, space, time, modularity, reference, initialization, and mutability. Where these properties do not suffice to guarantee a property at static time, dynamic checks may be added and the constraints on control flow prevent such dynamic checks from being avoided by the program.

The invention relates to the technical field of computer software, and discloses a bridging method and system for a cross-language interface of KaihongOS, a terminal and a storage medium. The method comprises the steps that an interface definition file is analyzed, and function declaration information of a target interface is obtained; analyzing a native interface definition file to obtain implementation information of a native interface corresponding to the target interface; and based on the function declaration information and the implementation information, according to a preset bridging rule, automatically generating a bridging function code conforming to a target operation environment, and bridging a cross-language interface through the bridging function code. According to the method, through automatic analysis and code generation, the bridging function code meeting the target operation environment is automatically generated by improving the code quality, and repeated labor and potential errors in manual writing are avoided. A preset bridging rule ensures the consistency and maintainability of codes, the technical threshold can be effectively reduced, and the project cycle is shortened.

The invention provides a program language conversion method and device, computing equipment, a computer readable storage medium and a computer program product, and relates to the technical field of computer software, the method comprises the following steps: converting a variable definition in a to-be-converted program file written based on a first programming language into a variable definition about a second programming language; generating a target grammar structure tree according to the to-be-converted program file, wherein the target grammar structure tree is an abstract grammar tree which represents a grammar structure of the first programming language and carries semantics; based on the target grammar structure tree, generating a state machine code of a second programming language; and according to the variable definition related to the second programming language and the state machine code of the second programming language, generating a target program file related to the second programming language. According to the application, automatic conversion from the Basic program code to the Pascal program code can be realized.

The translation device (1) includes a block extraction unit (11) that extracts blocks that are part of source code from the source code of a sequence control program, a translation target determination unit (14) that determines which of the blocks extracted by the block extraction unit (11) should be translated into a high-level language, and a translation unit (15) that translates the blocks that have been determined by the translation target determination unit (14) to be translated into the high-level language into the high-level language.

A computing system executing a data orchestration platform is provided. The data orchestration receives a manifest file wherein the manifest file defines one or more data processing configuration parameters. The data orchestration platform generates orchestration logic based upon the manifest file. Upon execution of the orchestration logic and responsive to receiving a first data, the data orchestration platform generates a data record based upon the first data and identifies one or more tasks to be executed based upon the data record. A first task of the one or more tasks to a task queue and the data orchestration platform causes a first data processing module to execute the first task, wherein execution of the first task causes the data processing module to generate an output. The data orchestration platform then updates the data record based upon the output of the first data processing module.

The present invention makes it possible to easily maintain an ST source file even when performing development in which two types of statements exist in the ST source file.　This call format conversion device comprises: an input unit that receives an argument format and a conversion target; an acquisition unit that reads an ST source file prior to conversion; an ST source file parser unit that has at least syntactic analysis and semantic analysis functions; a POU call management unit that manages argument information and call information with a function / function block definition output by the ST source file parser unit; and an output unit that outputs the ST source file after conversion. The ST source file parser unit performs syntactic analysis and semantic analysis on the ST source file that has been read and outputs, to the POU call management unit, call information and argument information of a function block definition and a function definition. The POU call management unit converts the received argument format on the basis of an argument designation in the call information and causes the output unit to output the ST source file after conversion.

A computer-implemented method for integrating chatbots into applications may include identifying one or more parameters of a code snippet in a first programming language and determining a usage of the code snippet. In one example, the method may also include generating an input for a chatbot in a second programming language based on the identified parameters and usage. Additionally or alternatively, the method may include receiving, from the chatbot, an output corresponding to the code snippet upon injecting the input into the chatbot. Various other methods, systems, and computer-readable media are also disclosed.

The invention discloses a conversion code generation method and device, equipment and a medium. The method comprises the steps that after it is detected that a development user uploads a to-be-converted business code and conversion annotation information to a preset code warehouse, all attributes of a source class and a target class of all sub-objects are read, and a source class attribute list and a target class attribute list of all the sub-objects are generated; for each sub-object, generating an attribute mapping table of the sub-object, and generating a bidirectional conversion code of the sub-object according to the attribute mapping table of the sub-object and the exchanged attribute mapping table of the sub-object; and storing the bidirectional conversion code of each sub-object into a target directory corresponding to the business code to be converted. According to the embodiment of the invention, after the development user uploads the to-be-converted business code and the conversion annotation information to the code warehouse, the bidirectional conversion code of each sub-object can be automatically generated, and the bidirectional conversion code is automatically stored in the target directory.

A source code translation system translates a source code snippet in an original source code programming language into distilled code that preserves the core semantics, logic flow, and data flow of the source code snippet in a language-independent format. The distilled code is then translated into a semantically-equivalent source code snippet in a different programming language by a decompiler. The decompiler is configured as a neural transformer model with attention pre-trained for translation using masked language modeling, denoising auto-encoding and multilingual program generation. The decompiler translates the distilled code into one of several different high-level programming languages.