179 results about "Language code" patented technology

A generative language model (e.g. large language model) may be used to generate programming language code. However, the generative language model may sometimes generate an output that is not compliant with the grammar of the programming language. In some embodiments herein, a generative language model may be modified to only generate an output that is grammar-compliant. A method may include generating a plurality of values using the generative language model, where each of the values is indicative of a probability of a respective token being a next token of a token sequence generated by the generative language model. A mask may be applied to the plurality of values. The mask may operate on each value that corresponds to a token not compliant with the grammar of the programming language to reduce or zero the probability of that token being the next token.

The various implementations described herein include methods and systems for generating applications and programs. In one aspect, a method of application generation includes presenting a user interface configured to assist a user with creating an application. The method also includes receiving, via the user interface, an indication of a set of components in a specification language and corresponding inputs, and generating, using the set of components and the corresponding inputs, code in a programming language, the code implementing the application. The method further includes presenting, via the user interface, at least a portion of the application.

The invention relates to an LLVM-based ST language compiling method and system, and the method comprises the following specific steps: taking an ST source code as input, and decomposing the ST source code into a tag stream token through an application lexical analyzer; constructing an abstract syntax tree AST by using the mark flow token through a syntactic analyzer; filling a global symbol table, and transmitting the global symbol table and the abstract syntax tree to the next stage; performing semantic analysis, including type check, generating an abstract syntax tree with additional semantic information, and transmitting the abstract syntax tree to the next stage; analyzing the abstract syntax tree with the additional semantic information, generating LLVM IR language codes and transmitting the LLVM IR language codes to the next stage; and the LLVM compiler compiles the LLVM IR code after receiving the LLVM IR code, and outputs an X86 target code and a PowerPC target code. According to the method, the transportability and the code generation efficiency of the ST language compiler can be remarkably improved, various target architectures are supported, meanwhile, good expansibility and flexibility are achieved, and complex industrial automation requirements can be met.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a set of target language code from a set of source language code. One of the methods includes obtaining input data that represents a set of source language code; and processing the input data to generate a set of target language code.

The invention discloses a cross-language code semantic alignment method, which constructs a shared semantic space through a unified abstract syntax tree (AST) and a graph matching network (GMN) so as to reduce the difference of different programming languages in syntax structure and node representation. The method comprises the following steps: (1) mapping a multi-language AST node to a unified general label set and performing structure enhancement; (2) performing node feature coding on the unified AST, and realizing cross-language interaction in combination with a cross-graph attention mechanism; (3) node representation is generated through intra-graph loop updating, and an overall semantic vector is obtained through global attention pooling; and (4) through comparative learning training in the shared space, the distance between semantically equivalent positive sample vectors is shortened, and the distance between non-equivalent negative sample vectors is shortened, so that the discrimination capability of cross-language semantic representation is enhanced. According to the method, the semantic consistency of the functional level can be effectively captured, and the accuracy and efficiency of cross-language code understanding, multiplexing and retrieval are remarkably improved.

The invention discloses a code change influence analysis method and device, equipment and a storage medium, and relates to the technical field of software, the method comprises the following steps: performing grammar analysis on a multi-programming language code to obtain a back-end abstract syntax tree and a front-end abstract syntax tree, and converting the back-end abstract syntax tree and the front-end abstract syntax tree into a uniform syntax tree structure through a front-end and back-end calling mapping chain; a cross-language dependency graph is constructed according to the unified syntax tree structure, the cross-language dependency graph is composed of nodes and edges, the nodes represent code entities, and the edges represent dependency relations; and when the code change of the bank business is detected, traversing the cross-language dependency graph through a graph traversal algorithm to obtain a code change influence result. Due to the fact that the back-end abstract syntax tree and the front-end abstract syntax tree of the multi-programming language codes are constructed and connected in series, the perfect cross-language dependency graph is constructed, the front-end link and the rear-end link are connected, cross-language tracking can be achieved when the codes of the banking business are changed, and the influence of code change can be accurately analyzed.

The invention discloses a lightweight cross-platform PLC (Programmable Logic Controller) multi-language code generation method, which relates to the field of industrial process control, and firstly provides a unified intermediate data model, and on the basis, a module for converting each PLC language into the unified intermediate data model is realized; on the basis of a standardized JSON data structure, a modularized unified intermediate language conversion chain is further supported, an analyzer and a generator communicate through a JSON interface, a cross-platform conversion path is supported, and unified intermediate language conversion from PLC to JSON to XML to ST to C is completed; the method comprises the following steps of: integrating an open source compiler and runtime, constructing an open and pluggable automatic execution framework, compiling a generated C code into a platform executable file by using an adaptive GCC tool chain aiming at different target platforms, and deploying a program generated by compiling to a soft PLC runtime system supporting multiple platforms. According to the method, a JSON intermediate data structure which is light in weight, unrelated to languages and uniform in structure and a matched conversion framework of the JSON intermediate data structure are constructed.

The invention discloses a multi-language code generation method based on self-supervised pre-training, which comprises the following steps: acquiring and cleaning multi-language code data to form a training corpus; the method comprises the following steps: representing code data as an abstract syntax tree, extracting a control flow diagram and a data flow diagram of the code data, and obtaining unified semantic representation through combination of a diagram encoder and a sequence encoder; designing a self-supervised pre-training task, and pre-training the semantic representation based on the training corpus; constructing a multi-language pre-training model based on the structure-improved recurrent neural tensor network and the multi-language embedding matrix; when a user inputs a natural language, generating a target language code by using the multi-language pre-training model; and target language code correction is carried out through conventional function testing and grammar checking. According to the method, multi-channel recursive combination and a hierarchical recursive expansion mechanism are combined with self-supervised pre-training, so that accurate generation and performability improvement of cross-language codes are realized.

The invention discloses an automatic construction system for a biological information analysis process, and the system comprises an intention understanding and semantic analysis module which is used for analyzing a natural language text inputted by a user into a structured task description meeting the requirements of a biological information analysis task; the knowledge graph and retrieval module is responsible for constructing a knowledge graph special for the bioinformatics field so as to provide knowledge retrieval and recommendation services; the process generation core module is used for receiving the structured task description, actively associating the knowledge graph with the retrieval module so as to supplement the field large model, and generating a biological information analysis target process language code; and the execution and monitoring module is used for guaranteeing workflow execution, full-life-cycle state monitoring, real-time fault diagnosis and intelligent self-healing decision making of target process language codes. According to the system, the executable analysis process can be directly generated according to the natural language requirement of the user, the tool compatibility and parameter validity are verified through the borrowed knowledge graph before execution, the dependency on the programming ability of the user is greatly reduced, and the process operation reliability is improved.

The invention relates to a contrast type self-adaptive knowledge distillation method oriented to large language model lightweight. The method comprises the following steps of selecting a general teacher model and a student model and performing initialization; training sets X constructed by using the same type of data are respectively used as inputs of a teacher model and a student model, and respective outputs of the two models are combined with original inputs to construct a mixed training sample; and constructing a double-branch contrast type loss function for training the student model, and finally obtaining the trained student model. The student model training method provided by the invention has the advantages of stable training, excellent performance, efficient resources and the like, is suitable for lightweight deployment scenes of language, code, vision and other multi-modal large models in a resource-constrained environment, and has good engineering application prospects and popularization values.

The invention provides a cross-language function consistency verification method and device, a storage medium and a program product, and the method comprises the steps: compiling a to-be-verified first language code into a first format file represented in the middle, and carrying out the modeling based on a predetermined formalized verification language, and generating a first formalized specification; compiling the second language code into a second format file represented in the middle, and modeling to generate a second formalized specification; verifying the consistency of the first format file and the first formalized verification language specification and the consistency of the second format file and the second formalized verification language specification through symbolic execution by using a formalized verification tool; and under the condition that the first formalized verification language specification and the second formalized verification language specification are consistent, verifying the equivalence of the first formalized verification language specification and the second formalized verification language specification through a formalized verification tool. According to the method, the problem that a traditional function consistency verification method cannot prove that codes realized by different languages are kept equivalent under all input conditions can be solved, and verification efficiency and completeness can be improved.

The invention belongs to the technical field of cross application of an artificial intelligence large language model and hardware design, and provides a hardware design-oriented special large model aided design method and system, and the method comprises the steps: obtaining an original code data set; the original code data set is preprocessed, and a hardware description language code database meeting the hardware design requirement is constructed and obtained; generating a multi-level code description pair data set based on the hardware language code database meeting the hardware design requirements and the constructed multi-level description mechanism; wherein the construction process of the multi-level description mechanism comprises the step of constructing a special large language model designed for hardware on the basis of a multi-level code description pair data set. The large language model can better meet the requirements of the hardware design field, the accuracy and reliability of the generated content are improved, and powerful support is provided for development of hardware design.

The invention discloses a cross-language voice interaction method and system based on multi-modal semantic understanding and a storage medium, and relates to the technical field of artificial intelligence and natural language processing. The method comprises the following steps: acquiring a source language speech stream, performing parallel double-flow feature extraction, extracting text semantic features through a mixed language code recognition model based on a unified phoneme space, and extracting acoustic features containing rhythm information at the same time; performing alignment fusion on the text and the acoustic features by using a cross-modal attention mechanism to generate multi-modal semantic representation; analyzing the explicit intention and the implicit emotion based on the representation, and generating a reply strategy and an emotion control label of the target language; and finally, synthesizing a target voice with a corresponding emotion color. According to the invention, the bottleneck that the traditional cascade architecture loses side language information is broken through, the precise understanding and strategic feedback of complex contexts such as Chinese-English mixed language codes (Code-mixing), anti-quiescence, hesitation and the like are realized, and the method is particularly suitable for transnational business negotiation and international customer service scenes.

The invention provides a code stream real-time rendering method and device, electronic equipment and a readable storage medium, and relates to the technical field of artificial intelligence such as large language models, hypertext markup languages, streaming processing and webpage generation. The method comprises the steps of obtaining a code stream of a hypertext markup language continuously output by a large model and used for generating a target webpage; performing streaming processing on the continuously acquired code stream according to the following preset streaming analysis mechanism: in response to the detected complete script tag, extracting the script to be processed corresponding to the complete script tag to a preset script management system; in response to the detected frame tag, processing and displaying the content of the frame tag in a sub-page created for the frame tag; the real-time streaming result is presented as a real-time web page rendering result corresponding to the portion of code received from the code stream. By applying the method, the rendering efficiency of obtaining the target webpage based on the code can be remarkably improved, and the waiting time of a user is shortened.

The invention relates to the technical field of software security, in particular to a high-credibility construction of a synchronous modeling language code generator and a formalized verification method and device thereof, electronic equipment and a medium, and the method comprises the following steps: obtaining a synchronous modeling language source program generated by the code generator; translating the synchronous modeling language source program to the abstract syntax tree; converting the abstract syntax tree into an intermediate representation, and converting the current intermediate representation into a next intermediate representation; and performing formalized verification on each transformation process until the translation process verification of all the language features of the synchronous modeling language source program is completed, and the formalized verification comprises the steps that each intermediate representation formalized definition has operation semantics meeting semantic certainty, and semantic retentivity is proved for the current intermediate representation transformation process. Therefore, the problems that related technologies only support partial language characteristics, compiling architecture design is simple, the formalized verification range is limited, and the requirements for complete modeling ability and high credibility are difficult to meet are solved.

An assert statement generator employs a neural transformer model with attention to generate candidate assert statements for a unit test method that tests a focal method. The neural transformer model is pre-trained with source code programs and natural language text and fine-tuned with test-assert triplets. A test-assert triplet includes a source code snippet that includes: (1) a unit test method with an assert placeholder; (2) the focal method; and (3) a corresponding assert statement. In this manner, the neural transformer model is trained to learn the semantics and statistical properties of a natural language, the syntax of a programming language, and the relationships between the code elements of the programming language and the syntax of an assert statement.

Example implementations include a method, apparatus, and computer-readable medium configured for generating codelets. An interface between a user and a large language model receives a natural language intent for a codelet to be executed within a network function to output one or more requested pieces of information from the network function. A codelet generation application generates a prompt to the large language model to write a codelet in an imperative procedural language to output the one or more requested pieces of information, the prompt including a hook point of the network function and one or more data fields of the network function associated with the one or more requested pieces of information. The application receives, in response to the prompt, imperative procedural language code for the codelet. The application statically verifies whether the procedural language code for the codelet satisfies constraints for execution within the network function.

The invention discloses a cross-language migration-based low-resource programming language corpus enhancement method, which comprises the following steps of: receiving a source language code, and driving a large language model to generate an initial target language code through a two-way retrieval mechanism fusing example guidance and knowledge constraint; secondly, constructing an automatic iterative repair closed loop by utilizing the feedback of a compiler, and carrying out self-correction on codes which fail to be compiled; thirdly, high-quality codes are screened out through automatic quality gating and fed back to a corpus and a knowledge base, and self-enhancement circulation of data and knowledge is formed; finally, the method further comprises an offline model evolution step based on grammar and semantic alignment driven by a compiler, a big language model is trained by collecting preference data generated by an online process, and the ability of the big language model to understand a target language is improved fundamentally. According to the method, the core problems of low quality, poor efficiency and lack of self-evolution ability of a low-resource programming language in code migration are solved.

Systems and methods are provided that incorporate a compiler configured to convert interpreted language code (e.g., Python) into native machine code. According to some embodiments, the system generates the native machine code into a format that is consistent with known infrastructure. The native machine code can be converted into a format based on a low level virtual machine “LLVM” infrastructure. In various embodiments, the system enables a compiler framework that improves execution of code for interpreted languages. According to one embodiment, the system can be tailored for execution on specific processors, for example, a graphics processing unit (“GPU”) that is optimized for highly parallel computations.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating cross-platform applications. One of the methods includes receiving a request to generate one or more applications configured to perform one or more algorithms, wherein each of the one or more applications is configured to perform the one or more algorithms on a respective corresponding platform of a plurality of platforms; obtaining input data that represents user code that comprises the one or more algorithms; and processing at least a set of target language code derived from the input data to generate, for each of the respective corresponding platforms, a respective buildable application.

A method (500) includes receiving, from an application (50) executing on a client device (110), at a speech service interface (200), configuration parameters (211) for integrating a speech service (250) into the application. The configuration parameters include a language pack directory (225) that maps a primary language code (235) to an on-device path of a primary language pack (110) of the speech service for use in recognizing speech in a primary language and each of one or more codeswitch language codes to an on-device path. The method also includes receiving audio data (102) characterizing an utterance (106) and processing, using a language ID predictor model (230), the audio data to determine that the audio data is associated with the primary language code. The method also includes processing, using the primary language pack, the audio data to determine a transcription (120) that includes one or more words in the primary language.

The invention discloses an intelligent question answering method and device based on a computational thinking model, equipment and a medium. The method comprises the following steps: training a computational thinking model by adopting a supervised fine tuning and reinforcement learning method to obtain a trained computational thinking model; the question data are input into the computational thinking model, the computational thinking model comprises a natural language reasoning module and a code reasoning module which are alternately performed, language instructions and code blocks are alternately generated in the dialogue context, and task reasoning is iteratively performed until answers corresponding to the question data are generated; and outputting an answer corresponding to the question data. According to the method, a computational thinking model is provided, the computational thinking model is an interactive reasoning framework, task reasoning is performed through interlaced natural language analysis and code execution, and iterative language-code circulation not only enhances the robustness and interpretability of the model, but also improves the accuracy of problem reasoning.

The invention provides a front-end code evaluation method and system, electronic equipment and a program product, and the method comprises the steps: obtaining a front-end code, carrying out the compiling and rendering of the front-end code, and obtaining a generated front-end page and a target front-end page; performing code extraction processing on the generated front-end page and the target front-end page, and performing redundant node cleaning and code structure tree generation processing on extracted markup language codes to obtain a generated code tree and a target code tree; performing tree structure and element content error calculation processing on the generated code tree and the target code tree to obtain a content structure error; performing visual structure error calculation processing on the generated front-end page and the target front-end page to obtain a visual structure error; and according to the content structure error and the visual structure error, determining and obtaining an evaluation result for generating the front-end code. The scheme can improve the generation accuracy of the front-end code, and can be widely applied to the technical field of artificial intelligence.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a set of target language code from a set of source language code. One of the methods includes obtaining input data that represents a set of source language code; and processing the input data to generate a set of target language code.

The invention discloses a Java-to-Cangjie code translation method based on a large model and compilation feedback, and particularly belongs to the technical field of software engineering program language processing, and the method comprises the following steps: step 1, performing structured semantic pre-training by constructing a grammar knowledge base of a target language, and injecting grammar prior knowledge of the target language; step 2, performing semantic enhanced supervision fine tuning training by constructing a high-quality data set containing semantic information, and enhancing semantic alignment and cross-language migration ability of the model; 3, introducing an AST structure perception embedded prompt mechanism in a parallel corpus supervision fine tuning training stage, and guiding the model to perform structure perception translation; and 4, establishing a compiler feedback repair loop, and iteratively correcting output based on error information to form a self-optimized closed-loop system. According to the method, an efficient training path is constructed, dependence on large-scale parallel corpora is effectively reduced, and an extensible and high-reliability technical path is provided for cross-language code translation of low-resource programming languages.

The invention discloses a warehouse level code translation method and device based on a large model, and is used for solving the technical problem that the existing warehouse level code translation method only relates to a function translation pair, so that a code translation task in a warehouse level context is poor in performance. The method comprises the steps that a tree-shaped analyzer is utilized, a large model is preset, and a target language code sample self-evolution knowledge base, a dependent use example self-evolution knowledge base and a successful translation function pair self-evolution knowledge base are constructed according to an obtained open source project, a to-be-translated warehouse and a plurality of historical successful translation function pairs; and outputting the target triple translation knowledge corresponding to each to-be-translated function, and outputting a warehouse level code translation result in combination with the obtained implementation code corresponding to each to-be-translated function and the generated warehouse architecture.

The invention provides a low-code application development method and platform, electronic equipment and a storage medium, in the low-code application development method, natural language data is utilized to train a meta-language programming model, and a low-code application development request submitted by adopting a natural language is responded through the meta-language programming model, so that the low-code application development efficiency is improved. Carrying out meta-language feature extraction processing on the low-code application development request to obtain a field specification language code described by adopting a meta-language, and extracting a functional component and / or a business component matched with the field specification language code from a code component library, or extracting a functional component and / or a business component matched with the field specification language code from a code component library; according to the method, functional components and / or business components matched with the domain specification language codes are extracted from a code component library, corresponding application software is generated through splicing, the extracted content is displayed in a visual mode, the function of quickly and accurately generating low-code applications through natural language description requirements is achieved, operation is easy, application is convenient, and the method is suitable for popularization and application. According to the method, the development efficiency is improved, particularly, the skill requirement for the programming technology of a user is reduced, and the method has wider applicability.

Provided in the embodiments of the present application are a speech recognition method, apparatus and device, and a storage medium and a program product. The method comprises: acquiring speech to be processed that includes a language code, and a corresponding first speech scenario; when the first speech scenario is a non-streaming scenario, inputting said speech into a trained first speech recognition model, so as to obtain first recognition text, wherein the first speech recognition model comprises a first encoder network and a first decoder network, and the first encoder network comprises a plurality of first linear layers that are respectively configured corresponding to different languages, and first selection layers connected to the first linear layers; and when the first speech scenario is a streaming scenario, inputting said speech into a trained second speech recognition model that matches the language code, so as to obtain second recognition text, wherein the second speech recognition model comprises a second encoder network and a second decoder network, and the second decoder network comprises a plurality of decoders that are respectively configured corresponding to different languages.

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.

The invention discloses an intelligent code writing and quality assurance system for multi-language code analysis, which relates to the technical field of intelligent code development and quality control and comprises a multi-language code analysis engine, an intelligent engineering management module, a self-adaptive compiler, an interactive console, a multi-dimensional instance library and a personalized configuration center, each module realizes real-time data interaction through a distributed data bus; the system supports more than 15 mainstream programming languages, new language support can be expanded through plug-ins, and the multi-language code analysis engine integrates static analysis, dynamic tracking and cross-language call detection functions. Static analysis identifies grammar errors and undefined variables through abstract syntax tree analysis. According to the intelligent code writing and quality assurance system for multi-language code analysis, the single-language limitation of a traditional system is broken through, conjoint analysis of programming languages is achieved, cross-language defect recognition accuracy is improved, and potential risks of multi-language projects are remarkably reduced.