485results about "Source to source" patented technology

Computer-implemented systems and methods use a Large Language Model (LLM) for converting a legacy computer program in a first language to a human-language description of the legacy computer program, which description can be validated as being an accurate description of the legacy computer program. Once validated, the human-language description can be converted, again using an LLM, to a computer program in a target programming language. An LLM can also be used to generate test scripts for the new target-language program to test the performance of the target-language program in a production environment. An LLM can also be used to reconcile outputs from the legacy program to the new target program, such as on a function-by-function basis. If the differences between the outputs (if any) are sufficiently negligible, the legacy computer program can be decommissioned, and the new, target language program can be used in production.

Systems and methods for efficiently translating program code from a source language to a target language. Input source code is parsed, using a processor device, into an Intermediate Representation (IR). A structural and semantic model of the source code are established by applying static analysis to the IR, and a program skeleton 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 built from the SSA form. The SDG is traversed to order translation tasks, and ordered tasks are translated into the target language using a Large Language Model (LLM). A translated program is generated by integrating translated code segments into a coherent program structure in the target language.

Apparatuses, systems, and techniques to determine a matrix multiplication algorithm for a matrix multiplication operation. In at least one embodiment, a matrix multiplication operation is analyzed to determine an appropriate matrix multiplication algorithm to perform the matrix multiplication algorithm.

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.

Large language model code translation error detection include receiving a code portion of a first programming language, and converting the code portion to a second programming language. A first accuracy of the converting of the code portion to the second programming language is calculated. A difference between the first accuracy and an historical accuracy of a conversion from the first programming language to the second programming language is determined. A potential error in the code portion of the first programming language is indicated based on the difference between the first accuracy and the historical accuracy being greater than a predetermined value is indicated.

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.

Using complexity metrics to assess code generated using artificial intelligence includes generating, by an artificial intelligence (AI) language model, output source code based on input source code; identifying respective complexity scores for the input source code and the output source code using one or more complexity metrics; and generating, based on an evaluation of the respective complexity scores, a validation score for the output source code.

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.

Systems and methods for streamlining risk modeling in software development using natively sourced kernels are described. The system may receive a native kernel for the first model, wherein the native kernel comprises a native code sample and a native description of the native code sample. The system may input the native code sample into an artificial intelligence model to generate a first output. The system may filter the first output based on the native description to generate a first validation assessment for the first model. The system may generate for display, in the user interface, the first validation assessment.

Embodiments of the present invention provide a system for performing multi-platform software code merging and migration using hybrid neural networks. The system is configured for identifying source files in a software code development environment, wherein each of the source files comprise software code associated with entity applications that is developed by one or more users in one or more software programming languages, translating the software code in each of the source files that is developed in the one or more software programming languages into a translated code that is in a first software programming language, merging the translated code associated with the software code in each of the source files into a unified code, wherein the unified code is in the first software programming language, automatically performing build and packaging of the unified code for deployment, and automatically deploying the packaged unified code into a software code production environment.

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.

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 provides a vector optimization algorithm translation method and device oriented to an RISC-V vector extension platform, and relates to the technical field of computer software cross-architecture translations. The method comprises the steps that source codes of vector optimization algorithms written by other platforms and translation cues are input into a large language model; outputting a translation code of a vector optimization algorithm oriented to the RISC-V vector extension platform; the translated codes are input into a compiler to be compiled; under the condition that compiling succeeds, a compiling product is linked to the test suite to obtain an executable program, and unit testing is conducted on the executable program on the RISC-V vector extension device; and under the condition that the unit test is passed, determining that the translation code is a first version of correct translation code of a vector optimization algorithm oriented to the RISC-V vector extension platform. According to the method, a vector optimization algorithm written by using a special vector language for other platforms is efficiently translated to the RISC-V vector extension platform.

Systems and methods for streamlining risk modeling in software development using natively sourced kernels are described. The system may receive a native kernel for the first model, wherein the native kernel comprises a native code sample and a native description of the native code sample. The system may input the native code sample into an artificial intelligence model to generate a first output. The system may filter the first output based on the native description to generate a first validation assessment for the first model. The system may generate for display, in the user interface, the first validation assessment.

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.

A source code repair system generates a prompt including a few-shot examples of code changes made previously to correct a particular source code vulnerability. The prompt is given to a large language model to generate repair code for a source code snippet having the same vulnerability. Code changes made for a particular source code vulnerability, in the form of code diffs, are clustered into groups of closely related code change embeddings. A select few of the code diffs in each group having a closest mean of the cluster are used as the few-shot examples for the corresponding source code vulnerability.

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 including quantifying pre-defined technical porting goal of a porting project into a technical value model, receiving selected decisions regarding the target system and porting tasks and current version of the porting system, executing traceable porting steps based on the selected decisions, porting tasks and current version of the porting system and creating software artefacts of the target system, evaluating software artefacts created or converted by each of the executed porting steps and / or evaluating test results by applying and / or simulating each ported software artifact, outputting references to the versions of created and / or converted and evaluated software artifacts into a porting log, and training the creation and evaluation component of the porting system with data of the porting log based on the decisions and the outcome of the porting project using porting mechanism to reach the pre-defined goal for the porting system.

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.

Using cross-compilation to determine translation accuracy of artificial intelligence generated code includes receiving a first code portion of a first programming language, converting the first code portion to a second code portion of a second programming language by a generative artificial intelligence model, and converting the second code portion to a third code portion of the first programming language by the generative artificial intelligence model. A translation accuracy score of the converting of the first code portion to the second code portion is calculated.

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 log processing method and device, equipment and a storage medium, and relates to the technical field of computers, and the method comprises the steps: obtaining a to-be-compiled source code compiled for embedded equipment on a host machine, and traversing the to-be-compiled source code to recognize a Log statement template to obtain an original Log statement template; generating a unique identifier for the original Log statement template; identifying a formatted character string in the original Log statement template to obtain a target formatted character string, and deleting a non-conversion description character sequence in the target formatted character string to obtain a compressed formatted character string; splicing the unique identifier and the compressed formatted character string to obtain a compressed Log statement template, and replacing an original Log statement template in the to-be-compiled source code with the compressed Log statement template to obtain a new code; and compiling the new code to obtain a mirror image file, and running the mirror image file on the target machine to generate compressed Log information. According to the method, the log compression process is simplified, the higher compression ratio is achieved, and then occupation of memory space resources is reduced.

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.

A graph representing the source code is computed, the graph comprising a plurality of nodes connected by edges, each node representing a software component of the source code and each edge representing a relationship between software components. A domain model of a domain related to the legacy code base is computed, the domain model comprising a plurality of concepts and relationships between the concepts. A node of the graph matching one of the concepts is identified. A functional role of model is assigned to the identified node. The knowledge of the assigned functional role is used to trigger any of: migration, deployment as a microservice, deployment as a containerised service, deployment as a cloud-native application, maintenance, refactoring.

Systems and methods for using compiler transforms to transform a non-local function into a local function are disclosed. The systems and methods perform a dynamic inter-procedural analysis before performing reverse-mode automatic differentiation. The dynamic inter-procedural analysis is performed to determine a maximum set of computer program information. A non-local to local transformation is applied to the determined maximum set of computer program information, and each original instruction is mapped to an optic that is represented as an opaque closure in the transformed local function.

To reduce burdens of verifying and / or correcting output data of a generative model.SOLUTION: A data processing system inputs verification result information, which is information regarding results of verifying output data in response to input of a first prompt or a second prompt to a generative model, and determines whether or not the verification result information satisfies conditions for generating the second prompt. If the verification result information satisfies the conditions, the data processing system uses the verification result information to generate the second prompt for the generative model.SELECTED DRAWING: Figure 2

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.