79 results about "Code refactoring" patented technology

Techniques for intelligently prompting an LLM to refactor code are disclosed. A code snippet is accessed. This code is identified as potentially comprising a reference to an out-of-compliance library. Context for the code snippet is generated. An LLM prompt is then built. This prompt will be fed to the LLM, and the prompt instructs the LLM to refactor the code snippet into modified code, which calls a compliant library. Output of the LLM is displayed. This output is based on the LLM operating in response to the LLM prompt. The output includes a proposed rewritten version of the code snippet.

The invention discloses a code reconstruction method, system and device based on function recognition and a medium, and the method specifically comprises the steps: scanning a project file topological structure, and constructing a knowledge graph based on a code import relation and a technology stack feature; performing context enhancement by analyzing the code function description and combining with the knowledge graph to generate a code modification instruction set; based on the code modification instruction set, in combination with data stream sensitivity marking and performance portrait analysis, evaluating the target code to obtain an evaluation result; based on an evaluation result, matching a historical optimal practice mode through a graph neural network, and generating an optimization scheme which retains an original design style; by comparing abstract syntax tree differences between original codes and verified optimization schemes, only functional logic nodes are replaced, and legal code style features are reserved. According to the method, the efficiency and quality of code reconstruction and optimization are improved, and a more convenient, efficient and intelligent code processing tool is provided for software developers.

The invention discloses a code reconstruction method and system based on data stream slicing and large language model driving, and belongs to the field of software engineering. The method comprises five parts of code preprocessing, data stream analysis, construction of cue words of a large language model, function testing and code review. According to the method, firstly, codes needing to be reconstructed are preprocessed and converted into a unified format; secondly, performing data stream analysis on the preprocessed codes, and dividing independent logic blocks; generating cue words according to a data flow analysis result and historical behaviors, and inputting the cue words into a large language model to obtain reconstructed codes; and finally, the codes before and after reconstruction are tested, and whether re-reconstruction is needed is detected, so that reconstruction of the long method codes is realized, and the accuracy and efficiency of code function division and method extraction are optimized.

Embodiments of the present invention provide a code reconstruction method and system, a computing device, a storage medium and a program product wherein the code reconstruction method comprises: performing code analysis on a to-be-reconstructed initial code to generate a target reconstruction scheme wherein the target reconstruction scheme comprises a code reconstruction plan and a code test plan; the code reconstruction plan comprises a function dependency relationship of the target code; analyzing a function dependency relationship in the code reconstruction plan, and generating at least two code reconstruction tasks corresponding to the initial code; executing the at least two code reconstruction tasks in sequence to obtain each initial code snippet, and iteratively optimizing each initial code snippet through static analysis and dynamic verification until candidate code snippets conforming to a function dependency relationship are generated; and compiling a test case of each candidate code snippet based on the code test plan, and testing each candidate code snippet based on each test case to obtain a plurality of target code snippets conforming to the function dependency relationship. And the coherence and the consistency of the reconstructed codes are ensured.

The invention relates to the field of software engineering and computer science, in particular to an intelligent code optimization and reconstruction system based on a large model, which adopts a comprehensive code analysis module to perform static and dynamic analysis and identify potential problems and complexity indexes in codes. Through an optimization suggestion generation module, personalized optimization suggestions are generated by using a neural network algorithm, and automatic code reconstruction is realized for different programming languages. And the system ensures the functional integrity of the reconstructed code through a reconstruction verification module, and provides a user feedback mechanism to optimize a subsequent strategy. Finally, the readability, maintainability and execution efficiency of the codes are remarkably improved, and the intelligent code optimization effect is achieved.

According to the satellite-borne software on-orbit reconstruction method and system supporting interface multiplexing, the working mode of a communication interface is expanded and designed, compatibility of normal data communication and a software reconstruction function is achieved, a set of complete load software on-orbit processing flow is provided, and the compatibility between the normal data communication and the software reconstruction function is improved. A load software reconstruction function is realized under the condition that large-scale development and modification are not carried out, so that satellite software in-orbit reconstruction is not limited to reconstruction of satellite service platform software. The method supports satellite-borne software parameter modification, dynamic library updating, software version replacement and multi-stage reconstruction, supports interface reuse, does not need a load to have a special code reconstruction channel, saves resources, simplifies design, and improves the flexibility, safety and reliability of the satellite load.

In some aspects, a management system is provided that interfaces with software code to execute a refactoring process. The process executes iteratively, where a generative AI component (e.g., an LLM) is given the task to make a repository pass a given validation iteratively. In each step, the LLM in provided as an input a list of failed validations to resolve and the ability to interact with the repository. In some examples, the LLM is configured to read source files, write source files, and retrieves information from other data sources such as the Internet or semantically-indexed source repositories, including the repository that the process is currently operating on, among other operations. Further, external APIs and databases can also be included in the process. The LLM will then execute one or multiple of these operations and process one or multiple files to generate modified and / or new code.

The invention particularly relates to a code writing, checking and modifying method based on a large model. According to the code writing, checking and modifying method based on the large model, a fine-tuning large language model is adopted to analyze user requirements, and multi-language initial codes are generated; executing deep code review through a static analysis engine, wherein the deep code review comprises syntax tree verification, security vulnerability mode matching and API dependency chain detection; executing the automatically generated test case set in an isolation environment, monitoring operation indexes in real time, and generating a defect thermodynamic diagram; and driving the large model to perform iterative code reconstruction based on a defect analysis result until it is confirmed that a preset quality standard is met. The code writing, checking and modifying method based on the large model is suitable for multiple languages, the defect rate of codes is reduced, the software development period is prolonged, the labor cost is reduced, and the vulnerability detection rate is greatly increased.

The invention provides a cloud native micro-service splitting and reconstructing method based on runtime behaviors. A three-layer architecture design is adopted, and the system comprises a dependency network graph construction module, a micro-service division module and a code reconstruction module. The dependency network diagram construction module models specific project source codes and user access operation into abstract representation capable of being processed by an algorithm, the micro-service division module divides a network diagram, obtains a splitting scheme for the dependency network diagram through visual page interaction with a user and manual modification, and inputs the splitting scheme into the code reconstruction module; and a code reconstruction module performs code reconstruction based on the splitting scheme and the dependency network diagram to obtain a complete and operable micro-service architecture code, so that the effect of automatically analyzing Spring Boot project source codes and monitoring the running project to generate the dependency network diagram is realized.

The invention relates to a code reconstruction-oriented Android application energy consumption evaluation method, which comprises the following steps of: S1, generating a test case set associated with a method for an application program; s2, selecting test cases associated with the method influenced by reconstruction from the generated test case set to form a test case set for evaluating the energy consumption of the Android application before and after reconstruction; s3, reconstructing a plurality of given reference applications to obtain each reference application and a corresponding reconstruction method; obtaining a corresponding test case set through methods of S1-S2; for each reference application and the corresponding reconstruction method thereof, extracting dynamic feature data of corresponding energy consumption behaviors before and after the application reconstruction, and estimating corresponding energy consumption data before and after the application reconstruction, so as to generate a code reconstruction-oriented energy consumption data set; s4, constructing a prediction model of the energy consumption of the Android application before and after code reconstruction; and S5, evaluating the energy consumption of the Android application by using the prediction model. The method is beneficial to accurately evaluating the energy consumption of the Android application before and after code reconstruction.

The invention discloses a code reconstruction method and system based on multi-agent collaboration. The method comprises the steps that a collaborative workflow engine obtains a source code to be reconstructed; the scheduling structure analysis agent analyzes the source code to obtain code structure information of the source code; scheduling an odor analysis agent to analyze the source code based on the code structure information and a preset index to obtain odor code information in the source code; the scheduling reconstruction strategy intelligent agent analyzes the code structure information and the peculiar smell code information to obtain a code reconstruction strategy; scheduling a reconstruction execution agent to execute the code reconstruction strategy to obtain a reconstruction code; and outputting the reconstructed code. The technical problems that a traditional code reconstruction tool has limitation of single-point decision making, rule stiffness and the like, and the quality of reconstructed codes is poor are solved.

The invention discloses a source code reconstruction method and device, equipment and a medium. The method comprises the following steps: acquiring a to-be-processed source code and reconstruction performance description content; determining at least one target code snippet in the to-be-processed source code according to a code reconstruction standard; according to the to-be-processed source code, generating a calling relation and an input and output relation corresponding to the to-be-processed source code; aiming at each target code snippet, reconstructing the target code snippets according to the calling relation and the input and output relation to obtain reconstructed code snippets; and replacing the target code snippets in the to-be-processed source codes with the reconstructed code snippets, wherein the to-be-processed source codes before replacement and the to-be-processed source codes after replacement have the same functions. According to the embodiment of the invention, the source code upgrading efficiency and automation degree of the original system can be improved.

The invention discloses an intelligent contract code reconstruction method, device and equipment and a readable storage medium, and is applied to the technical field of computers, and the method comprises the following steps: obtaining an intelligent contract code reconstruction request; obtaining a multi-agent architecture pre-constructed based on the intelligent contract code reconstruction workflow; the multi-agent architecture is a multi-agent architecture based on a large language model; and based on the smart contract code reconstruction request, performing smart contract code reconstruction by using a multi-agent architecture to obtain a reconstructed smart contract code. According to the method, based on the standard workflow of intelligent contract code reconstruction and in combination with a multi-agent architecture of a large language model, a universal intelligent contract code reconstruction workflow and multiple agents cooperate together, so that semantic understanding of a complex intelligent contract scene and implementation of a reconstruction target are improved, the method is an automatic reconstruction process, and the implementation efficiency is improved. A user does not need to customize a template, and the limitation that manual participation and implementation can only be achieved in a specific code mode is reduced.

The invention relates to the technical field of network security, and discloses a program-level code similarity analysis method and device based on semantic slicing, and the method comprises the steps: constructing a program-level target function call graph based on a target source code; matching the target function call graph with a standard function call graph to obtain a plurality of isomorphic sub-graphs; performing semantic slicing on each function node in the first sub-graph and the second sub-graph to obtain a first slicing result of the first sub-graph and a second slicing result of the second sub-graph; and evaluating the slice similarity between each function in the first slice result and each function in the second slice result, and determining the similarity of the target source code according to the slice similarity. According to the method, the user-defined function call graph for eliminating system function interference is constructed, an isomorphic sub-graph matching algorithm is combined, multi-dimensional features are comprehensively considered, the accuracy of similarity calculation is improved, effective support is provided for practical application scenes such as code reconstruction and security audit, and the guarantee of code quality and security is improved.

Techniques are disclosed for intelligently prompting LLM to reconstruct code. A code snippet is accessed. The code is identified as potentially including a reference to a non-compliance library. A context is generated for the code snippets. Then, an LLM prompt is constructed. The hint is to be fed to the LLM, and the hint instructs the LLM to reconstruct the code snippets into modified code, the modified code invoking the compliance library. And displaying the output of the LLM. The output operates based on the LLM in response to the LLM cue. The output includes a proposed rewritten version of the code snippet.

The application discloses a robot skill primitive library automatic generation method and system based on a large language model, and relates to the fields of artificial intelligence and robot software engineering. The method comprises the following steps: adopting a double-level function extraction strategy to extract modular functions and process scripts in a robot code library from a robot task-level python file; based on the extracted robot function code, adopting a preset thinking chain prompt word to guide a large language model to generate global semantic description and to distinguish functions with the same name; based on the function metadata of the robot task level after the same name function is distinguished, adopting a preset thinking chain prompt word to guide the configured large language model to perform perception type code reconstruction with embedded field constraints; based on the reconstructed code, adopting a preset thinking chain prompt word to guide the configured large language model to perform adaptive iterative code evaluation and optimization closed loop to obtain a robot skill primitive library. The application can automatically and reliably generate a robot skill primitive library.

The invention provides an airborne software heterogeneous reconstruction system, which belongs to the technical field of airborne software, and comprises an application construction module used for software source code development, software source code transplantation and software source code reconstruction; the container construction module is used for containerization packaging of software source codes; the mirror image construction deployment module is used for generating a container mirror image according to the function combination required by the system every time; the resource management module forms a data access interface of each module; the operation management module is used for monitoring and data collection; the resource library module is used for storing a software source file, a container source file, a container mirror image, a target system operation process and result data; the node management container module is used for computing node management, computing node network management and networking management of multiple computing nodes; the function container module is used for realizing system application functions; and the application management container module is used for operation scheduling and management of the functional container modules and data communication among the multifunctional container modules.

The invention provides a program verification case construction method and device, a storage medium and equipment. The method comprises the following steps: acquiring a program code of a program to be verified; wherein the program code comprises a plurality of program units; according to the input value and the return value of each program unit, determining a calling relationship between the program units, and generating a program calling sequence according to the calling relationship, the program calling sequence comprising at least two program units having the calling relationship in the program codes; inputting the program calling sequence into a preset use case generation model, and generating a reconstruction code based on the code reconstruction prompt information by using the use case generation model; and inputting the reconstruction code into a use case generation model, and generating a verification use case for the to-be-verified program by using the use case generation model based on the use case generation prompt information. According to the scheme, the coverage rate of the verification case on the function statements in the to-be-verified program is increased, and the reliability of the verification case is improved.

This invention discloses a Python dependency library API migration method based on a large language model. This method employs a multi-proxy strategy based on the large language model to simulate and automate the process of a programmer migrating an API. Using unstructured web page data easily accessible online, the method performs five steps: user intent recognition, network resource discovery, random walk-based information gathering, migration, and testing and verification. Ultimately, it migrates the user-specified dependency library API from the user-provided code. The multi-proxy strategy allows the large language model to perform well on complex tasks like API migration, while the intent recognition and testing and verification steps effectively control unreliable outputs caused by the large language model illusion problem. This invention can be applied to software engineering tasks such as code updates, code refactoring, and legacy code handling, helping to reduce human costs in software development and maintenance.

This invention provides an artificial intelligence-based software refactoring method. The method includes: acquiring the current code data and user data of the software to be refactored; determining a target refactoring strategy based on the current code data and user data; generating refactoring code data corresponding to the target refactoring strategy based on a large language model; and refactoring the software based on the refactoring code data to obtain the target software. This invention analyzes code data and user data, considering both the code problems of the software to be refactored and user business expectations, to generate a target refactoring strategy that balances technical optimization and business needs. This avoids the problem of the separation between technical optimization and business goals in traditional methods, ensuring that the refactored code accurately matches the user's actual business scenario while optimizing technical indicators, reducing the cost of secondary modifications due to misunderstandings of requirements, and thus improving the accuracy and efficiency of software refactoring.

An application migration method from client-server architecture to blockchain architecture is provided. The application migration method includes the following steps: S1: modeling an application program of a conventional client-server architecture and abstracting the application program to a program call tree; S2: defining a taint propagation rule for taint analysis by taking key data as tainted data, to acquire all taint variables in the application program; and S3: identifying a key module of the application program on the program call tree according to a tainted path, and setting a code refactoring rule to perform code refactoring on a method in the key module according to a special program structure.

The invention relates to the technical field of computers, and discloses an automatic upgrading system of a server component, a readable medium and electronic equipment. The system comprises an upgrade change module, a test deployment module, a test verification module, a component release module and a result detection module, sub-code branch creation and automatic code reconstruction can be performed in a dependency mode, and a sidecar process is determined in a sidecar mode; creating and deploying a test environment to perform functional test and code verification; after that, the code merging request is initiated in the dependency mode to request to merge the update of the sub-code branches into the main code branch, or online gray release is gradually performed to full release in the sidecar mode, and the result detection of the component upgrade progress is performed, so that the compatibility and stability of the component upgrade can be comprehensively detected and verified, and the reliability of the component upgrade is improved. And moreover, codes do not need to be modified manually in the whole process, the automation degree is high, and high-efficiency and low-cost centralized closed-loop component upgrading is realized.

The embodiment of the invention discloses a code reconstruction method and device, equipment and a storage medium, and the method comprises the steps: responding to a reconstruction configuration instruction, and displaying a reconstruction configuration interface; receiving reconstruction configuration information corresponding to the reconstruction configuration item, and determining a to-be-reconstructed first code file according to the reconstruction configuration information; according to a pre-generated reconstruction change information set, determining a first code to be changed in the first code file and reconstructing the first code into a second code to obtain a second code file; wherein the reconstruction change information set comprises at least one piece of reconstruction change information, and each piece of reconstruction change information is used for matching the to-be-changed code and performing code reconstruction on the to-be-changed code. By means of the method, intelligent rapid searching of the to-be-reconstructed code file is achieved, and the risk of missing the to-be-reconstructed code file is better avoided while the labor cost is saved; in addition, automatic matching and reconstruction of the to-be-changed code in the to-be-reconstructed code file are achieved, and the code reconstruction rate and the reconstruction accuracy are greatly improved.

The invention relates to a file input and output proxy method and system compatible with a general kernel mirror image specification, and the method comprises the steps: providing an interface compatible layer consistent with VFS operation in a kernel space, converting a file operation request into a Netlink protocol message, sending the Netlink protocol message to a user space for execution, maintaining the original calling semantics through a synchronous coordination mechanism, and carrying out the operation of the Netlink protocol message; according to the method, the GKI specification requirement can be met by zero modification of the drive code, the problem of large-scale code reconstruction in the process of smoothly migrating the Android system to the GKI specification is effectively solved, and the development workload and the risk of introducing new defects are reduced.

The present application relates to the field of data processing, and particularly relates to a code automatic generation method and system based on an algorithm model, comprising: obtaining a first incompatibility degree between different prompt words according to a first reconstructed code of each prompt word; retrieving a plurality of reference knowledge with the greatest similarity to each prompt word from a vector database and obtaining a second reconstructed code; obtaining a second incompatibility degree between different prompt words according to the difference between the first reconstructed code and the second reconstructed code of different prompt words; recalculating the similarity between each reference knowledge and each prompt word according to the reduction amount of the second incompatibility degree relative to the first incompatibility degree, and then obtaining a final reconstructed code. When facing the situation of prompt word incompatibility and insufficient code reconstruction ability of a large model, the present application can utilize all prompt words for code reconstruction as much as possible, so that the generated reconstructed code can meet the demand of the new energy monitoring and intelligent control field.

The invention provides a Swift code automatic recommendation and conversion method and device, equipment and a medium, and belongs to the technical field of computer software. The method comprises the following steps: receiving an original Swift code input by a user; converting the original Swift code to obtain a serialized code corresponding to the original Swift code; extracting to-be-input features from the serialized codes; the to-be-input features are input into a machine learning model obtained through pre-training, the confidence coefficient that an original Swift code output by the machine learning model belongs to a non-conventional code is obtained, and when the confidence coefficient is larger than a preset threshold value, the machine learning model further outputs a conventional serialization code corresponding to the original Swift code; and performing code reconstruction based on the conventional serialization code to obtain a target recommendation code conforming to the Swift grammar. According to the method, the deep semantic understanding function can be added, so that the habitual code snippet recommendation capability and the code automatic conversion capability are improved.

The invention discloses a Python dependency library API migration method based on a large language model. According to the method, a multi-agent strategy is adopted on the basis of a large language model, and the process when a programmer migrates the API is simulated and automated. According to the method, non-structured webpage data which can be easily obtained through a network is used, and migration of a dependency library API specified by a user in codes provided by the user is finally completed through five steps of user intention recognition, network resource discovery, information collection based on random walk, migration and test verification. The multi-agent strategy can enable the large language model to obtain good performance on complex tasks similar to API migration, and the intention recognition and test verification steps can effectively control unreliable output caused by the illusion problem of the large language model. The method can be applied to software engineering related tasks such as code updating, code reconstruction and heritage code processing, and the labor cost in software development and maintenance can be reduced.

This invention discloses a system performance optimization method and related equipment based on layered decoupling. When a kernel service node detects a cross-process service call, it obtains the call context information, filters target communication data that meets key service filtering conditions from the call context information to identify the current scenario and determine the current scenario identifier data. The hardware abstraction layer service performs performance parameter matching on the current scenario identifier data and performs business logic processing based on the parameter matching results to generate performance tuning data. The kernel service node then executes the performance optimization processing corresponding to the performance tuning data. This invention enables system performance optimization based on layered decoupling, significantly improving performance optimization efficiency. Through the layered decoupling architecture, it greatly reduces the difficulty and workload of code refactoring and adaptation, improves the maintainability and upgrade efficiency of the performance optimization framework, and can be widely applied in the field of system performance optimization technology.

The invention relates to the technical field of artificial intelligence, and can be applied to software code reconstruction scenes in financial and medical scenes. The invention provides a software reconstruction method, system and device and a storage medium, and the method comprises the steps: carrying out the context analysis of a target code entity through a planning agent, and generating a structured reconstruction plan based on an analysis result; generating a reconstructed code by the generation agent according to the reconstruction plan; the compilation agent performs compilation verification on the reconstructed code, and when compilation fails, compilation error information is fed back to the generation agent for iterative correction; and the test agent performs function test on the reconstructed code after successful compiling, and feeds back test failure information to the generation agent for iterative correction when the test fails. The method can be applied to financial / medical software code reconstruction scenes, and the success rate of software reconstruction is improved through multi-agent cooperation and double feedback closed loops of compiling and testing.