156results about "Source to source" patented technology

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 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.

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 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.

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.

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.

Disclosed herein are embodiments of systems, methods, and products comprises a server for efficiently processing electronic requests. The server receives a plurality of predictive computer models and generates a specification file for each model by parsing the source code of each model. When the server receives an electronic request, the API layer of the server validates the request by verifying the inputs of the request satisfying validation codes in the specification file of the corresponding model. If the electronic request is invalid, the server imputes valid values for the request and sends the imputed values to the model execution layer. Within the model execution layer, the server utilizes an integrated development environment of a third-party server to call the function of the corresponding model. The model execution layer transmits the function's output results back to the API layer, which transmits the output results to the user device.

Legacy program applications written in a SQL-like programming language such as FOCUS can be automatically converted to modern languages such as Python / PySpark. The FOCUS files are parsed and translated into SQL statements which can be stored, along with associated metadata, in a transformation configuration file. The configuration file can then be converted into Python / PySpark code.

The translation program causes, as a receiving unit (13), a PLC program development environment (10) that translates a first source code (21), which is described in a first language, of a program to be executed in order to control a device to be controlled by a PLC (30) into a second source code (22), which is described in a second language, and causes a translation unit (12) to function, said receiving unit (13) receiving a specification of a monitoring variable. The translation unit (12) translates the first source code (21) into the second source code (22). A translation unit (12) converts a code relating to a variable of a value stored in a shared memory between a PLC (30) and a device to be controlled in a first source code (21) to a code that refers to the shared memory when the variable is a monitoring variable, and converts the code to a code that refers to a non-shared memory when the variable is different from the monitoring variable.

The application relates to the computer technical field, in particular to a page processing method and device, electronic equipment and storage medium, adopting a standard language, converting at least one original jump component based on an original language contained in an original application page into corresponding target jump components respectively; determining page jump indication information between the original application page and each other application page based on the at least one original jump component; adopting the standard language, converting at least one original page element based on the original language into corresponding target page elements based on the element conversion mode corresponding to each original page element contained in the original application page; and sending the obtained at least one target jump component, page jump indication information and at least one target page element to the search server for associated storage. In this way, the richness of search results can be improved.

Techniques for transforming code modules to different programming languages are disclosed. A system accesses a first non-code representation of a first code module expressed in a first programming language and parses the first non-code representation to identify a nested data element of the first non-code representation that represents a nested expression of the first code module. The system executes a transformation technique to transform the nested data element, in the first non-code representation, to a first non-nested data element in the first non-code representation. The system modifies the first non-code representation based on one or more attributes of a second programming language to generate a second non-code representation suitable for representing code modules in the second programming language. The system generates a second code module based at least on the second non-code representation.

In an example embodiment, rather than use large language model (LLM) to directly generate desired computer code, an intermediate representation is generated by the LLM. The LLM is used to generate the portion of the computer code that cannot be computed programmatically (which may be called the “creative” part for purposes of the present disclosure). The intermediate representation can then be fed into a separate programmatic component that compiles the intermediate representation into compilable computer code. This fine-tuning may involve, for example, sanitizing the intermediate representation, enhancing the intermediate representation, and formatting the intermediate file, as well as modifying the intermediate representation based on a feature set.

A program translation system may generate a first program source internal representation expressed using a source language representation of a source programming language and a first program target internal representation that is expressed using a target language representation of a target programming language. The system may interpret the first program source and target internal representations with first program data to generate a first program source and target results. The system may, when the source and target results are inconsistent, modify the target language representation. The system may, when the source and target results are consistent, generate a second program source internal representation of a second program. The system may use the second program source internal representation to generate a second program target internal representation of the second program. The system may use the second program target internal representation to generate a second program in the target programming language.

A method, for example a computer-implemented method, for processing data associated with a data structure for a computer program, for example for a product such as a vehicle, comprising: determining initial information that characterizes a transformation of a first data structure of a first definition into a second data structure of a second definition, wherein the second definition is different from the first definition, and, optionally, transforming the first data structure into the second data structure based at least on the initial information.

A neural transcompilation model is tested with a set of syntax unit tests to determine the syntax elements of a source code program written in a source programming language that fail to translate properly into a target programming language. The syntax elements having a translation defect is identified and ranked according a translation failure rate. The neural transcompilation model is then fine-tuned with training samples of the syntax elements having the highest translation failure rates and their paired correct translation in order to teach the model to learn the association between the syntax elements of a source programming language causing translation defects and its correct translation in a target programming language.

The application discloses a WSDL-based service end API automatic construction method and device and medium, relates to the technical field of information system development, and comprises the following steps: determining a type mapping rule according to a JSON data structure; obtaining a Java data class file through automatic recursive processing of a Java naming rule based on the JSON data structure and the type mapping rule; performing abnormal state analysis on the Java data class file to determine a file exception solution method; obtaining a standard annotation through service class annotation marking according to the Java data class file; performing service skeleton construction conforming to a JAX-WS specification on the standard annotation to determine a directly identifiable service class; and performing dynamic compilation on the directly identifiable service class to obtain a dynamically deployed service. The application solves the technical problems that the prior art cannot automatically deduce a Java class from an arbitrary JSON example and cannot automatically construct and deploy a WebService service.

To provide an operation for detecting the API compatibility between software versions.SOLUTION: The operation may include receiving an input related to a software application. The operation may further include determining first information. The operation may furthermore include extracting a set of data from a source of at least one web base on the basis of the determined first information. The operation may further include conducting a set of operations including at least one pattern search operation for the extracted set of data and generating a compatibility result. The operation may also include controlling a display device on the basis of the generated compatibility result. The display device is formed to display support information as a notification of the compatibility of at least one API or function used in the source code of the software application for the second version of the software.SELECTED DRAWING: Figure 18

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).

A Custom Resource (CR) is generated within a code repository of a legacy application based on intercepted Application Programming Interface (API) calls to an imperative API of the legacy application. The intercepted API calls relate to a particular type of entity. The CR is defined by a Custom Resource Definition (CRD) associated with the particular type of entity. The CR is one of a plurality of CRs of the code repository that collectively define a correct state of the legacy application. Responsive to a difference between a current state of the legacy application and the correct state of the legacy application, an automated operator makes an imperative API call to the imperative API of the legacy application that modifies the current state of the legacy application to match the correct state of the legacy application.

Systems and methods for translating a first encoded language to a second encoded language train a machine learning (ML) model on a first encoded language specific data set associated with the first encoded language, where the ML model is trained to translate one or more code sets of the first encoded language to respective one or more code sets of the second encoded language; generating various unit test cases by using the ML model, where the unit test cases run the one or more code sets of the second encoded language in parallel with the one or more code sets of the first encoded language; iteratively testing and refining the ML model until a maturity threshold value is reached; and upon reaching the maturity threshold, containerizing the one or more code sets of the second encoding language into the one or more applications.

The invention discloses a syntactic expression and interpretation method for an engineering platform. The interpretation method comprises the steps that a demand model is converted into a syntactic script, the syntactic script is converted into an XML script, and the XML script is loaded to an ASP platform. A script language is designed to describe the digital platform and can become a unified abstraction layer of the digital platform, the configuration complexity in platform engineering is remarkably reduced, and meanwhile control over a bottom layer technology is reserved.

A socket assembly for a semiconductor integrated circuit (IC) chip is provided. The socket assembly includes a socket frame including a frame body defining a frame opening sized to receive a chip. The socket assembly also includes a socket cartridge including a cartridge body defining cavities each sized to receive a test probe therein. The socket assembly further includes a manifold assembly including a manifold defining a channel extending inside the manifold, a manifold inlet coupled with the manifold and sized to receive gaseous cooling fluid, and a manifold outlet coupled with the manifold and defining an aperture in fluid communication with the channel and the frame opening. The socket assembly defines a socket outlet in fluid communication with the frame opening, and the socket assembly defines a fluid path between the manifold inlet and the socket outlet.

A distillation system extracts knowledge from a large pre-trained sequence-to-sequence neural transformer model into a smaller bi-encoder. The pre-trained sequence-to-sequence neural transformer model is trained to translate data from a first domain into a second domain on a large corpus. A teacher model is generated from the pre-trained model by fine-tuning the pre-trained neural transformer model on a smaller translation task with true translation pairs. The fine-tuned model is then used to generate augmented data values which are used with the true translation pairs to train the bi-encoder. The bi-encoder is used for perform cross-domain searches.