12500results about "Code compilation" patented technology

An example operation may include one or more of training a generative artificial intelligence (GenAI) model based on execution of the GenAI model on software libraries and descriptions of intent of the software libraries, receiving a first set of software libraries and a second set of software libraries, identifying a first software library within the first set of software libraries that includes redundant functionality with a second software library within the second set of software libraries based on execution of a generative artificial intelligence (GenAI) model on the first and second sets of libraries, and displaying an identifier of the first and second software libraries via a user interface.

The embodiment of the invention provides a cross-chip platform compiling tool chain method which comprises the following steps: scanning a target platform architecture specification file to extract instruction set features, constructing a platform difference matrix based on the instruction set features to record instruction formats and system call differences of different chip platforms, and normalizing a multi-language syntax tree into a unified intermediate representation; constructing a cross-platform compiler to convert the intermediate representation into a target code; designing a unified intermediate language based on a platform difference matrix to support multi-language compilation; creating an instruction mapper to convert the intermediate representation into a target platform instruction; identifying a target architecture type through a platform identification code; designing a test case to verify a compiling result, and deploying the optimized target program to a target platform; according to the method and the device, the expandability and the code quality of the compiling tool chain can be improved by constructing the unified intermediate presentation layer and the optimization framework, and more efficient cross-platform support is realized.

The invention provides a large language model-driven code review and optimization method, which belongs to the field of computer science, and comprises the following steps of: firstly, performing intensive training on a model through rich code samples and accurate manual annotation to ensure that the model has accurate code understanding and analysis capability; and then, performing deep analysis on an input source code by utilizing the trained large language model, and constructing an abstract syntax tree (AST) to capture deep semantic information of the code. And finally, the model automatically analyzes the codes, identifies potential defects, non-standard coding practices and optimization possibilities, and generates specific optimization suggestions, including code repair schemes and guidance following optimal practices, according to identification results, so that efficient support is provided for developers, and the quality of software products is ensured.

A computer-implemented method includes generating, based on a representation of a tensor mapping between an input tensor and an output tensor, a list of mappings from elements of the input tensor to elements of the output tensor, and generating groups of mappings from the list of mappings, where each of the groups of mappings corresponds to a respective set of matrix multiplications, a matrix transpose, or both. The computer-implemented method also includes generating a respective expression for each of the groups of mappings and generating code for summing results of the respective expressions, where each respective expression includes the respective set of matrix multiplications, the matrix transpose, or both.

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 generating code for artificial intelligence models without requiring training data that is specific to model components and objectives. For example, the system may receive an original version of a rule engine. The system may input the original version, using a first input condition, into a regeneration model to generate a first regenerated version of the rule engine. The system may determine whether the first regenerated version includes a first hallucination based on comparing the first regenerated version to alternative versions of the rule engine, wherein each of the alternative versions were generated using a respective alternative input condition. The system may, in response to determining that the first regenerated version includes the first hallucination, determining whether the first hallucination comprises a positive mutation.

A system allows generation of information used by an integrated development environment using a machine learning-based language model, for example, a large language model. The integrated development environment is for developing applications, e.g., database applications. The system receives a natural language request for information related to development of the database application. The system determines contextual information describing a development task associated with the database application and generates a prompt for a machine learning based language model based on the natural language request and the contextual information and receives a response. The system extracts the information related to development of the database application from the response. In response to a natural request, the system may generate a database query, provide a resultset by executing the database query, automatically determine a type of chart and generate one or more charts for visually displaying the result.

Methods and systems for assessing a software bill of materials (SBOM) include building a knowledge graph from repositories, using function fingerprints of software packages in the repositories. Dependencies of an application are identified using function fingerprints of the application and comparing to function fingerprints of the software packages. A quality score for an SBOM of the application is generated based on a comparison of the identified dependencies to claimed dependencies of the SBOM. An action is performed responsive to the quality score.

Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: examining image layers of a container image and generating, in dependence on the examining, layer dependency relationship data that specifies layer dependency relationships of the container image; storing in a container repository the layer dependency relationship data that specifies layer dependency relationships of the container image; in response to receipt of a download request that specifies a targeted layer of the container image, analyzing relationship data of the layer dependency relationship data; in dependence on the analyzing, identifying a subset of image layers of the container image preceding the targeted layer; and establishing a deployment container image in dependence on the identified subset of image layers.

The invention relates to the technical field of software development, and particularly provides a self-adaptive code annotation updating method and system, a terminal and a storage medium, and the method comprises the following steps: confirming that codes are updated, extracting an updated code set, and combining and constructing an abstract syntax tree for the codes; performing semantic analysis on the abstract syntax tree by using a deep learning model to generate a semantic vector, and constructing a program dependency graph based on the semantic vector; analyzing a dependency relationship among code elements in the program dependency graph by utilizing a graph neural network; and generating a code annotation based on the semantic vector and the dependency relationship between the code elements by using a natural language generation model. According to the method, the real-time performance and the accuracy of the code document are ensured, the readability and the maintainability of the code are greatly improved, and the maintenance cost is reduced.

Techniques for leveraging a large language model (LLM) in software development are described. A description of an error message associated with a software system hosted by the multi-tenant provider network is received. An LLM is prompted with an analysis prompt to generate an analysis of the error message, the analysis prompt including the description of the error message, and the analysis of the error message is received from the LLM. The LLM is prompted with a suggested resolution prompt to generate a suggested resolution to a cause of the error message, the suggested resolution prompt including the description of the error message and the analysis of the error message, and the suggested resolution is received from the LLM. A change to resolve the cause of the error message is sent to an originator of the received description, the change based at least in part on the suggested resolution.

The invention provides a low-code rule engine method and platform supporting dynamic service adjustment, and relates to the technical field of data processing.The method comprises the steps that original rule data are obtained, and the original rule data are subjected to structured conversion and unified format specification; generating a visual rule component, presenting rule logic through a graphical interface, supporting business personnel to edit and modify without coding, and converting a modification result into component rule data; performing real-time analysis and verification on the component rule data to obtain operation rule data; performing version identification on the operation rule data, and storing the operation rule data in a rule warehouse; based on the current service context, matching an applicable version, automatically selecting the operation rule data, and loading the operation rule data to a rule execution engine for execution; the autonomy and accuracy of the low-code rule engine are improved.

Systems, methods, and devices that relate to routing requests to large language models (LLMs) are disclosed. In one example aspect, the system receives session-specific data elements in response to a request to generate an output using LLMs. The system determines a hierarchy of operational constraints including privacy protocols and performance requirements. Weights for a multi-variable optimization are dynamically updated using the session-specific data elements. The system executes the multi-variable optimization across candidate LLMs that satisfy privacy constraints and optimize performance constraints. Based on the optimization, at least one candidate LLM is selected and the request is routed to it. In response to performance feedback, the system automatically selects a different LLM to improve one constraint, resulting in degradation of another constraint.

A computer-implemented, machine learning method for preprocessing code for performance portability includes extracting performance critical code segments from an application and obtaining input data. Ground truth data is generated based on the input data and the application. Original code of the application is transpiled using a large language model (LLM) into a tensor computation language (TCL) candidate. Correctness of an implementation of the TCL candidate is verified using the ground truth data. The method has applications including, but not limited to, use cases in medicine / healthcare, and other artificial intelligence applications for preprocessing and optimizing code for performance portability.

Techniques and systems described herein relate to monitoring executions of computer instructions on computing devices based on learning and generating a control flow directed graph. The techniques and systems include determining a learned control flow directed graph for executable code of an application by observing executions of transitions during an observation period and determining destinations of indirect transfers based on the learned control flow directed graph. Next a disassembly of the executable code is determined based on the learned control flow directed graph, the destinations of the transfers, and the executable code.

Provided herein is a method for reviewing code. The method can comprise parsing the code to generate a structural representation of the code, wherein the structural representation comprises a graph representative of the code. The method can comprise processing the code and the structural representation to generate a context for the code based at least in part on the graph. The method can comprise processing the context and a set of prompts to generate a set of contextualized prompts, wherein at least two prompts in the set of prompts are specific for different categories of issues. The method can comprise prompting a first set of language models with the set of contextualized prompts to generate a set of issue reports. The method can comprise prompting a second set of language models to generate a set of validated issue reports comprising a set of non-hallucinated issue report.

A method includes generating a modeling language diagram that is indicative of a compiled version of program code. The modeling language diagram includes at least one node corresponding to at least one function in the program code. The method also includes identifying at least one function signature associated with anode state transition between nodes in the modeling language diagram. The method also includes identifying, using a large language model, a particular policy to attribute to the at least one function signature associated with the node state transition. The method further includes performing, using the large language model, a policy compliance operation that ensures a particular portion of the program code complies with the particular policy. The particular portion of the program code is associated with the node state transition. Performing the policy compliance operation includes generating a policy-compliant version of the particular portion of the program code.

The invention discloses a low-code development automatic generation method based on a large language model, which comprises the following steps: collecting natural language description information input by a user, and preprocessing; inputting the standardized demand corpus set into a large language model, and executing semantic understanding and context modeling; matching the low-code component library based on the structured semantic representation to generate component assembly description information; generating and verifying an engineering skeleton according to the assembly description information, and outputting an executable low-code application initial version; running the executable low-code application initial version, and monitoring and analyzing execution difference to generate an increment adjustment instruction; and inputting the increment adjustment instruction into the large language model, performing reconstruction and adaptive optimization, and outputting an executable low-code application final version. According to the method, large language model semantic understanding and adaptive optimization technologies are fused, automatic generation and continuous optimization of low-code applications are realized, and the method has the advantages of intelligence, high precision and engineering reliability.

Embodiments of systems, methods, and apparatuses for heterogeneous computing are described. In some embodiments, a hardware heterogeneous scheduler dispatches instructions for execution on one or more plurality of heterogeneous processing elements, the instructions corresponding to a code fragment to be processed by the one or more of the plurality of heterogeneous processing elements, wherein the instructions are native instructions to at least one of the one or more of the plurality of heterogeneous processing elements.

The present disclosure provides a binary code similarity detection system based on hard sample-aware momentum contrastive learning, comprising: a preprocessing device for transforming the binary code into tokens for neural networks; a feature extracting device for using the tokens to generate final representation embeddings for the binary code; and a similarity detection device for using the representation embeddings to detect the binary code similarity.

The invention discloses an intelligent risk identification and self-adaptive repair method, system and device for a software supply chain and a medium, belongs to the field of network security and automatic software engineering, and aims to solve the technical problem of how to accurately and comprehensively identify software code supply chain risks including code snippets. A reliable and efficient automatic closed-loop repair scheme is provided, and the technical defects that in the prior art, the software code supply chain recognition range is limited, the repair process is rigid and the reliability is low are overcome. Analyzing the declarative dependency; meanwhile, semantic traceability based on artificial intelligence is carried out on the code snippets, and a global software material list is generated; and performing intelligent mapping on the software components in the global software bill of materials and the vulnerability database to identify risks.

The invention provides a business module source code generation method and system based on large model business reasoning, and belongs to the technical field of code generation. The business module source code generation method comprises the steps that semantic analysis is conducted on business requirements, and a business association graph with weights is constructed; performing module division, and decomposing a complete task into a plurality of sub-tasks; calling a coordination agent, distributing a corresponding module code generation agent based on a large model for each decomposed subtask, and injecting context information to generate a corresponding module code; wherein the module code generation agent learns a corresponding relationship between tasks and injected context information and codes in advance based on field self-adaptive staged training; and calling the verification agent to perform code quality verification. The method has the beneficial effects that the module division is performed based on the business association graph with the weight, the multi-agent collaborative code generation is constructed by adopting the field-adaptive staged training, and the quality of the generated code is verified, so that the code generation efficiency and quality are improved.

The invention provides an enterprise production data micro-service granularity optimization method, a medium and a system, belongs to the technical field of production data management, and establishes a micro-service granularity evaluation equation set by obtaining a dependency relationship and business flow data of micro-services in an enterprise production data system, constructing a coupling degree and cohesion degree matrix and establishing a micro-service granularity evaluation equation set. According to the method, a hybrid optimization algorithm is adopted, the search direction of a grey wolf algorithm and group evolution of a genetic algorithm are combined, and a composite fitness function containing system response time, resource utilization rate, maintenance cost and micro-service granularity balance degree is constructed. And continuously updating the micro-service division scheme through iterative optimization until the preset number of iterations is reached and the composite fitness function meets the optimization target, and finally outputting the optimal micro-service division scheme. The technical problem that the micro-service granularity is difficult to determine in the prior art is solved according to the division scheme of multiple indexes such as the maintenance cost and the granularity balance degree.

The embodiment of the invention belongs to the field of data processing, and relates to an interface calling method and device, computer equipment and a storage medium, and the method comprises the following steps: carrying out natural language processing on user request text information to obtain request semantic representation and key entity information; performing semantic matching on the request semantic representation and the key entity information according to the interface knowledge graph to obtain a target program interface document; performing structured analysis and information extraction on the target program interface document to obtain document key information; performing code generation based on a code generation model according to the document key information and the request semantic representation to obtain a target calling code; testing the target calling code to obtain a code test result; and adjusting the code generation model according to a code test result, and generating an adjusted target code to be applied to the target program interface for interface calling. According to the method and the device, accurate and effective application program interface calling can be realized according to the request text input by the user.

The invention provides a code peculiar smell detection method and system based on AST code measurement and code semantics. The method comprises the following steps of: firstly, constructing a FA-AST (Flow-Augmented Abstraction Syntax Tree) by analyzing a code snippet, and extracting three types of AST-based code metrics, namely a structure metric, a syntactic metric and a semantic metric from the FA-AST; then, a deep learning model is used for capturing contextual information and a dependency relationship of the code from the source code so as to extract code semantics; and finally, by fusing AST-based code measurement and code semantics, inputting a classification model to carry out training and testing so as to identify the peculiar smell in the code and evaluate the severity of the peculiar smell. Compared with software code measurement extracted by a traditional static code analysis tool, the AST-based code measurement provided by the invention is more comprehensive and easier to extract, and has obvious advantages in the aspect of improving the code odor detection precision and efficiency.

The present application discloses an intelligent inspection system and method for a power distribution network. The intelligent inspection system for the power distribution network comprises: acquiring an inspection analysis model; converting a model parameter into a specific tensor form, to determine whether a network structure of the inspection analysis model matches a network structure supported by a target porting platform; if the two network structures match, on the basis of the network structure supported by the target porting platform, converting the tensor form of the inspection analysis model into a target tensor, and rewriting an inconsistent portion in an operator structure of the inspection analysis model, to complete recompiling of the inspection analysis model; if the two network structures do not match, performing tensor regeneration for a non-matching model parameter type of the inspection analysis model, and performing regeneration for a non-matching operator structure of the inspection analysis model, to complete recompiling of the inspection analysis model; and pushing the recompiled inspection analysis model to the target porting platform, causing the target porting platform to perform inspection analysis on the basis of an inspection task.

Computer devices, systems and methods for transforming converting and evaluating high complexity computer science optimization problems using quantum and quantum inspired data transformation approaches and corresponding computer data structures are proposed, useful in specific situations, where computational complexity at scale prohibits alternative approaches, the approaches to solving the transformed problems yielding acceptable accuracy output despite a technical tradeoff in potential loss in accuracy. The transformed computer problem can then be solved using specialized quantum or quantum inspired computing architectures. The optimization problem outputs can be converted into specific data messages routed for automatically invoking downstream data processes and data subroutines.

The invention discloses an embedded system construction method and device, and the method comprises the steps: loading a configuration file which is a. Config file generated based on configuration parameters selected by a user through a visual configuration interface; when the CONFIGCONANAUTOUPDATE flag bit is detected to be in an activated state, the Conan warehouse data center is accessed, the latest compatible version of each Conan software package dependency item is inquired, and a semantic version constraint analysis algorithm is adopted to update a version identifier in the configuration file into the latest compatible version; generating a description file containing a dependency item list based on the updated configuration file, and generating a dependency lock file through a Hash locking mechanism; and importing the pre-compiled binary package into the construction environment, calling the corresponding pre-compiled binary package according to the version information and the Hash check value in the dependency lock file in the firmware compiling process, and executing hierarchical compiling operation.

The invention discloses an auxiliary code review method and system based on a large language model, and the method comprises the steps: analyzing a submitted code file based on a static analysis technology and an abstract syntax tree AST extraction tool, and extracting key elements; reviewing the code through a pre-constructed company internal and external knowledge base by relying on a knowledge graph technology to obtain a reviewing result; performing context analysis on the code by adopting a large language model to obtain an analysis result; integrating the review result and the analysis result, and generating a review report through NLP; a containerization technology is adopted to be integrated with plug-ins of mainstream development tools, and private deployment is supported. According to the method, the efficiency, accuracy and safety of code review can be greatly improved, the workload of manual review is reduced, and automation and intelligentization of the code review process are realized.

A code insertion engine predicts one or more statements of a programming language to be inserted at an insertion point in between existing source code statements of a source code program being edited. The code insertion engine extracts the surrounding context of the insertion point which includes the source code immediately preceding and the source code immediately following the insertion point. The code insertion engine uses a neural expansion model and a neural selector model to predict the one or more statements most likely to be inserted into the insertion point that are syntactically and semantically consistent with the surrounding context of the existing program.