Implementation method and system of intelligent coding auxiliary system

Abstract

The invention relates to the technical field of computer software development, in particular to an implementation method and system of an intelligent coding auxiliary system.The implementation method comprises the following steps that a multi-language support framework is constructed, specifically, a special grammar parser is constructed for each supported programming language and used for parsing source codes and generating an abstract syntax tree AST; developing a code converter to realize a code conversion function among different programming languages, so that a user can process codes of various programming languages in a unified environment; the implementation method and system of the intelligent coding auxiliary system have the beneficial effects that high-quality code snippets can be quickly generated through intelligent code generation and reconstruction functions, the manual coding time is remarkably shortened, and the development efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of computer software development, and in particular to an implementation method and system of an intelligent coding assistance system. Background Art

[0002] In the process of software development, coding efficiency and code quality have always been the focus of developers. Traditional manual coding is not only time-consuming and labor-intensive, but also prone to errors. In recent years, with the development of artificial intelligence technology, intelligent coding assistance tools have gradually become one of the effective means to improve development efficiency.

[0003] However, existing intelligent coding assistance tools often only support a single programming language and lack a comprehensive evaluation mechanism for code quality, making it difficult to meet the development needs of large and complex projects. For example, existing intelligent coding assistance tools usually only provide basic code completion functions and cannot intelligently recommend best practice code snippets based on context; at the same time, these tools often lack code style and standardization checks, making the generated code difficult to maintain.

[0004] Therefore, it is particularly important to develop an intelligent coding assistance system that can support multiple languages, has intelligent code generation and refactoring capabilities, and can perform code quality detection. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for implementing an intelligent coding assistance system to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a method for implementing an intelligent coding assistance system, the method comprising the following steps:

[0007] Build a multi-language support framework, including building a specialized syntax parser for each supported programming language to parse source code and generate an abstract syntax tree (AST);

[0008] Develop code converters to implement code conversion functions between different programming languages, allowing users to process codes in multiple programming languages ​​in a unified environment.

[0009] Preferably, the method further comprises the following steps:

[0010] Collect, clean, and preprocess a large number of high-quality code samples to build a code dataset for training;

[0011] A neural network model based on the Transformer architecture is used to train the code dataset to generate complete code snippets from natural language descriptions or partial code snippets.

[0012] Design generation strategies, including probability-based sampling and rule-based constraints, to ensure the quality and diversity of generated code.

[0013] Preferably, the method further comprises the following steps:

[0014] Develop a code analysis engine to analyze code structure and logic, and identify redundancy and bad design in the code;

[0015] Based on the code analysis results, intelligently recommend code refactoring solutions, including but not limited to variable renaming, function splitting and merging;

[0016] Integrated optimization algorithms are used to further improve code performance, automatically identify performance bottlenecks in the code and provide optimization solutions.

[0017] Preferably, the method further comprises the following steps:

[0018] Use static code analysis technology to detect potential errors in the code, such as null pointer exceptions and type mismatches;

[0019] Supports code style checking in multiple programming languages ​​to ensure that the code follows unified programming standards;

[0020] Integrated security vulnerability scanning tools detect security vulnerabilities in the code, such as SQL injection and XSS attacks, and provide repair suggestions.

[0021] Preferably, the method further comprises the following steps:

[0022] Provides a real-time updated code editor that supports instant display and modification of code snippets and dynamically generates code snippets based on user input;

[0023] Display detailed code quality reports, including error lists and optimization suggestions, to help developers quickly locate problems and provide solutions;

[0024] Allow users to customize interface themes and font size according to personal preferences.

[0025] A system for implementing an intelligent coding assistance system, applied to an implementation method of the intelligent coding assistance system, the system comprising:

[0026] Syntax parsers for multiple programming languages. Each parser can accurately parse the syntax structure of the corresponding programming language and convert the source code into an abstract syntax tree (AST) for subsequent code processing and analysis.

[0027] Code converter converts code between different programming languages ​​to achieve cross-language code interoperability, allowing users to process codes in multiple programming languages ​​in a unified environment.

[0028] Preferably, a code intelligent generation module is included, which specifically includes:

[0029] Data preprocessing unit, used to collect, clean and preprocess a large number of high-quality code samples to build a code dataset for training;

[0030] A neural network model based on the Transformer architecture, which is trained to generate complete code snippets from natural language descriptions or partial code snippets;

[0031] Generation strategies, including probability-based sampling and rule-based constraints, are used to ensure that the generated code is of high quality and diversity, meeting specific programming needs and design patterns.

[0032] Preferably, a code reconstruction and optimization module is included, which specifically includes:

[0033] Code analysis engine, which analyzes the structure and logic of the code, identifies redundancy and bad design in the code, and provides optimization suggestions;

[0034] Refactoring suggestion unit, which intelligently recommends code refactoring solutions based on code analysis results, including variable renaming, function splitting and merging, to improve code readability and maintainability;

[0035] Optimization algorithms are used to further improve code performance, automatically identify performance bottlenecks in the code, and provide specific optimization solutions, such as loop optimization and conditional expression simplification.

[0036] Preferably, a code quality detection module is included, which specifically includes:

[0037] Static code analysis tools are used to detect potential errors in the code, such as null pointer exceptions and type mismatches, and improve the robustness of the code;

[0038] The code style checking unit supports code style checking in multiple programming languages, ensuring that the code follows unified programming standards and improving the readability and consistency of the code;

[0039] Security vulnerability scanning tool, used to detect security vulnerabilities in the code, such as SQL injection, XSS attack, provide repair suggestions, and ensure the security of the application.

[0040] Preferably, it includes user interface and interaction design, specifically including:

[0041] Real-time editor, which supports instant display and modification of code snippets, dynamically generates code snippets based on user input, and provides instant code feedback;

[0042] Code quality report display unit, which displays detailed code quality reports, including error lists and optimization suggestions, to help developers quickly locate problems and provide solutions;

[0043] Personalization options allow users to customize interface themes and font sizes according to their personal preferences, providing a comfortable user experience.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The implementation method and system of the intelligent coding assistance system proposed in the present invention can quickly generate high-quality code snippets through intelligent code generation and reconstruction functions, significantly reducing the time of manual coding and improving development efficiency; the integrated code quality detection module can automatically detect and prompt potential errors and bad programming habits, effectively improving the robustness and maintainability of the code; the user interface is friendly and intuitive, supporting multiple programming languages, reducing the difficulty of cross-language development, and simplifying the entire software development process; personalized setting options allow users to customize the interface according to their needs, improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose and technical solution of the present invention clearly and completely described, and the advantages more clearly understood, the embodiments of the present invention are further described in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are part of the embodiments of the present invention, rather than all of the embodiments, and are only used to explain the embodiments of the present invention, and are not used to limit the embodiments of the present invention. All other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] For example, see Figure 1 The present invention provides a technical solution: a method for implementing an intelligent coding assistance system, the method comprising the following steps:

[0049] Build a multi-language support framework, including building a specialized syntax parser for each supported programming language to parse source code and generate an abstract syntax tree (AST);

[0050] Develop code converters to implement code conversion functions between different programming languages, allowing users to process codes in multiple programming languages ​​in a unified environment.

[0051] The process also includes the following steps: collecting, cleaning, and preprocessing a large number of high-quality code samples to construct a code dataset for training; using a neural network model with a Transformer architecture to train the code dataset to generate complete code snippets from natural language descriptions or partial code snippets; and designing generation strategies, including probability-based sampling and rule-based constraints, to ensure the quality and diversity of the generated code.

[0052] The following steps are also included: developing a code analysis engine to analyze code structure and logic, and identify redundancy and poor design in the code; intelligently recommending code refactoring solutions based on the code analysis results, including but not limited to variable renaming, function splitting and merging; integrating optimization algorithms to further improve code performance, automatically identify performance bottlenecks in the code, and provide optimization solutions.

[0053] It also includes the following steps: using static code analysis technology to detect potential errors in the code, such as null pointer exceptions and type mismatches; supporting code style checks in multiple programming languages ​​to ensure that the code follows unified programming standards; integrating security vulnerability scanning tools to detect security vulnerabilities in the code, such as SQL injections and XSS attacks, and providing repair suggestions.

[0054] It also includes the following steps: providing a real-time updated code editor that supports instant display and modification of code snippets, and dynamically generates code snippets based on user input; displaying detailed code quality reports, including error lists and optimization suggestions, to help developers quickly locate problems and provide solutions; allowing users to customize interface themes and font size settings according to their personal preferences.

[0055] Embodiment 2, based on embodiment 1, proposes a system for realizing an intelligent coding assistance system, which is applied to a method for realizing an intelligent coding assistance system. The system includes:

[0056] 1. Multi-language support framework

[0057] Syntax parser: A dedicated syntax parser is built for each supported language, which can accurately parse the syntax structure of various programming languages. For example, for the Java language, a parser is designed that can recognize syntax elements such as class definition, method declaration, and variable assignment; for the JavaScript language, a parser is designed that can recognize syntax elements such as function definition, object literal, and array operation. These parsers can convert source code into an Abstract Syntax Tree (AST) for subsequent processing.

[0058] Code Converter: A code converter has been developed that can convert code between different programming languages ​​to achieve cross-language support. For example, when a user enters a piece of JavaScript code in a Java environment, the code converter can convert it into equivalent Java code. This allows users to process code in multiple programming languages ​​in a unified environment.

[0059] 2. Intelligent code generation module

[0060] Data preprocessing: Collect a large number of high-quality code samples, clean and preprocess them, and build a code dataset for training. The dataset contains typical code snippets in various programming languages, covering common programming tasks such as data structure operations and algorithm implementation.

[0061] Model training: Use a neural network model with a Transformer architecture to train a model that can generate complete code snippets from natural language descriptions or partial code snippets. The model training process includes the following steps:

[0062] Encoding phase: Encode natural language descriptions or partial code snippets into vector representations.

[0063] Decoding phase: Generate target code snippets based on the encoded vectors.

[0064] Loss calculation: The difference between the generated code snippet and the real code snippet is calculated as the loss function during training.

[0065] Backpropagation: The model parameters are adjusted through the backpropagation algorithm to minimize the loss function.

[0066] Generation strategy: A set of generation strategies, including probability-based sampling and rule-based constraints, are designed to ensure the quality and diversity of the generated code. For example, the diversity of the generated code can be controlled by setting a threshold, or the generated code can be ensured to conform to a specific design pattern by using rule constraints.

[0067] 3. Code refactoring and optimization module

[0068] Code Analysis Engine: We developed an engine that can analyze code structure and logic to identify redundancy and bad design in the code. The engine can detect common problems such as duplicate code, complex conditional judgments, and provide corresponding optimization suggestions.

[0069] Refactoring suggestions: Based on the code analysis results, intelligent code refactoring solutions are recommended, including but not limited to variable renaming, function splitting and merging, etc. For example, when multiple similar functions are detected in a piece of code, the refactoring suggestion may suggest merging these functions into a common function and passing different behaviors through parameters.

[0070] Optimization algorithms: A series of optimization algorithms are integrated to further improve code performance, such as loop optimization, conditional expression simplification, etc. These algorithms can automatically identify performance bottlenecks in the code and provide specific optimization solutions.

[0071] 4. Code quality detection module

[0072] Static code analysis: Use static code analysis techniques to detect potential errors in the code, such as null pointer exceptions, type mismatches, etc. These analysis tools can find potential problems before the code runs, helping to improve the robustness of the code.

[0073] Code style check: supports code style checks for multiple programming languages ​​to ensure that the code follows unified programming standards. For example, for Java language, you can check whether it follows the standards in Google Java Style Guide; for JavaScript language, you can check whether it follows the standards in Airbnb JavaScript Style Guide.

[0074] Security vulnerability scanning: integrated security vulnerability scanning tools to detect security vulnerabilities in the code, such as SQL injection, XSS attacks, etc. These tools can help developers find and fix security vulnerabilities in a timely manner to ensure the security of the application.

[0075] 5. User interface and interaction design

[0076] Real-time Editor: Provides a real-time updated code editor that supports instant display and modification of code snippets. The editor can dynamically generate code snippets based on user input and allow users to view and modify the generated code instantly.

[0077] Code quality report: Displays a detailed code quality report, including a list of errors, optimization suggestions, etc. The report can help developers quickly locate the problem and provide solutions.

[0078] Personalization: Allow users to customize interface themes, font sizes, etc. according to their personal preferences. For example, users can choose a dark theme to reduce eye fatigue, or adjust the font size to adapt to different screen sizes.

[0079] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for implementing an intelligent coding assistance system, characterized in that: The method comprises the following steps: Build a multi-language support framework, including building a specialized syntax parser for each supported programming language to parse source code and generate an abstract syntax tree (AST); Develop code converters to implement code conversion functions between different programming languages, allowing users to process codes in multiple programming languages ​​in a unified environment.

2. The method for implementing an intelligent coding assistance system according to claim 1, characterized in that: The following steps are also included: Collect, clean, and preprocess a large number of high-quality code samples to build a code dataset for training; A neural network model based on the Transformer architecture is used to train the code dataset to generate complete code snippets from natural language descriptions or partial code snippets. Design generation strategies, including probability-based sampling and rule-based constraints, to ensure the quality and diversity of generated code.

3. The method for implementing an intelligent coding assistance system according to claim 1, characterized in that: The following steps are also included: Develop a code analysis engine to analyze code structure and logic, and identify redundancy and bad design in the code; Based on the code analysis results, intelligently recommend code refactoring solutions, including but not limited to variable renaming, function splitting and merging; Integrated optimization algorithms are used to further improve code performance, automatically identify performance bottlenecks in the code and provide optimization solutions.

4. The method for implementing an intelligent coding assistance system according to claim 1, characterized in that: The following steps are also included: Use static code analysis technology to detect potential errors in the code, such as null pointer exceptions and type mismatches; Supports code style checking in multiple programming languages ​​to ensure that the code follows unified programming standards; Integrated security vulnerability scanning tools detect security vulnerabilities in the code, such as SQL injection and XSS attacks, and provide repair suggestions.

5. The method for implementing an intelligent coding assistance system according to claim 1, characterized in that: The following steps are also included: Provides a real-time updated code editor that supports instant display and modification of code snippets and dynamically generates code snippets based on user input; Display detailed code quality reports, including error lists and optimization suggestions, to help developers quickly locate problems and provide solutions; Allow users to customize interface themes and font size according to personal preferences.

6. An implementation system of an intelligent coding assistance system, applied to the implementation method of the intelligent coding assistance system according to any one of claims 1 to 5, characterized in that: The system comprises: Syntax parsers for multiple programming languages. Each parser can accurately parse the syntax structure of the corresponding programming language and convert the source code into an abstract syntax tree (AST) for subsequent code processing and analysis. Code converter converts code between different programming languages ​​to achieve cross-language code interoperability, allowing users to process codes in multiple programming languages ​​in a unified environment.

7. The implementation system of the intelligent coding assistance system according to claim 6, characterized in that: Includes a code intelligent generation module, which specifically includes: Data preprocessing unit, used to collect, clean and preprocess a large number of high-quality code samples to build a code dataset for training; A neural network model based on the Transformer architecture, which is trained to generate complete code snippets from natural language descriptions or partial code snippets; Generation strategies, including probability-based sampling and rule-based constraints, are used to ensure that the generated code is of high quality and diversity, meeting specific programming needs and design patterns.

8. The implementation system of the intelligent coding assistance system according to claim 6, characterized in that: Including code refactoring and optimization module, which specifically includes: Code analysis engine, which analyzes the structure and logic of the code, identifies redundancy and bad design in the code, and provides optimization suggestions; Refactoring suggestion unit, which intelligently recommends code refactoring solutions based on code analysis results, including variable renaming, function splitting and merging, to improve code readability and maintainability; Optimization algorithms are used to further improve code performance, automatically identify performance bottlenecks in the code, and provide specific optimization solutions, such as loop optimization and conditional expression simplification.

9. The implementation system of the intelligent coding assistance system according to claim 6, characterized in that: Includes a code quality detection module, which specifically includes: Static code analysis tools are used to detect potential errors in the code, such as null pointer exceptions and type mismatches, and improve the robustness of the code; The code style checking unit supports code style checking in multiple programming languages, ensuring that the code follows unified programming standards and improving the readability and consistency of the code; Security vulnerability scanning tool, used to detect security vulnerabilities in the code, such as SQL injection, XSS attack, provide repair suggestions, and ensure the security of the application.

10. The implementation system of the intelligent coding assistance system according to claim 6, characterized in that: Including user interface and interaction design, specifically including: Real-time editor, which supports instant display and modification of code snippets, dynamically generates code snippets based on user input, and provides instant code feedback; Code quality report display unit, which displays detailed code quality reports, including error lists and optimization suggestions, to help developers quickly locate problems and provide solutions; Personalization options allow users to customize interface themes and font sizes according to their personal preferences, providing a comfortable user experience.

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