Intelligent code generation system and method based on multi-model game algorithm

Abstract

The invention relates to an intelligent code generation system and method based on a multi-model game algorithm. The intelligent code generation system comprises an AIModel Manager module, a CodeGenerator module, a ProjectManager module and a Flask Web service module. The AIModel Manager module is used as a core controller of a multi-model game; the CodeGenerator module is used as a template engine for code generation; the ProjectManager module is focused on the full life cycle management of the project file; and the Flask Web service module is used for constructing a user interaction interface. According to the method, the core pain points of unstable code quality, incomplete project structure, low evaluation efficiency and the like in the prior art are solved, a software development normal form of man-machine cooperation is redefined, and a systematic solution is provided for improving the development efficiency, guaranteeing the code quality and reducing the technical threshold.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of artificial intelligence and software engineering, specifically relating to an intelligent code generation system and method based on a multi-model game algorithm. Background Technology

[0002] The current state of code generation technology reveals significant limitations, urgently requiring innovative breakthroughs. Existing tools primarily rely on single AI models (such as large language models) or pre-defined rule engines, resulting in fundamental deficiencies in the quality and stability of their generated results. The output of a single model is highly limited by the coverage of training data and inherent biases in the algorithm architecture, making it difficult to effectively handle complex business logic or edge-case scenarios. For example, when generating data processing scripts, insufficient training data often leads to the omission of exception handling logic, causing the code to easily crash in real-world environments. Furthermore, traditional tools lack dynamic optimization capabilities, failing to adjust generation strategies based on real-time changes in user needs or contextual information. This results in output code that is often out of touch with actual application scenarios, requiring developers to invest significant time in manual adjustments. The most prominent issue is the lack of dynamic correction; more importantly, there is the problem of project structure integrity. Mainstream tools only focus on generating fragmented core functional code, severely lacking a complete project skeleton, including essential dependency management configuration files (such as pom.xml or package.json), auxiliary utility functions, unit testing frameworks, and standardized technical documentation, forcing developers to supplement these elements, which not only reduces development efficiency but also introduces the risk of human error. In the evaluation stage, existing technologies rely heavily on manual review, which is time-consuming, lengthy, and highly subjective, making it difficult to establish objective and quantifiable quality standards, thus hindering the application of code generation technology in large-scale production environments.

[0003] Although related technologies such as Alibaba Cloud's Tongyi Qianwen series of large language models have made progress in natural language understanding and basic code generation, their single model architecture still cannot solve the aforementioned systemic defects. At the same time, multi-agent systems have shown the potential to improve task quality through cooperation or competition in the fields of game AI or automated control, but this mechanism has not been effectively transferred to the field of code generation, resulting in a significant gap in technological development.

[0004] Therefore, how to organically integrate the differentiated advantages of multiple models and build a code generation framework with dynamic optimization capabilities and automated evaluation mechanisms has become a core technical challenge that urgently needs to be overcome in this field. An innovative solution that can realize intelligent game between models, dynamic screening, and project completion is urgently needed. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide an intelligent code generation system and method based on a multi-model game algorithm. It deeply integrates the cutting-edge achievements of artificial intelligence and software engineering, aiming to achieve intelligent optimization and automated evaluation of the code generation process through a multi-model dynamic game mechanism.

[0006] The technical problem solved by this invention is achieved through the following technical solution: An intelligent code generation system based on a multi-model game theory algorithm is disclosed. The system integrates multiple high-performance heterogeneous AI models through an AI model manager. When a user describes development requirements in natural language, the system initiates the multi-model game theory algorithm. In the initial generation phase, all connected models independently produce code solutions based on the same requirements. Subsequently, in the evaluation game phase, each model switches roles and cross-scores the code generated by each other according to a preset multi-dimensional quality index system. Finally, in the optimization and integration phase, the system intelligently selects the optimal code snippet based on the comprehensive score matrix generated by the game theory and automatically performs project structure completion operations—including generating a directory hierarchy that conforms to industry standards, adding necessary dependency declaration files, inserting standardized documentation comments, building a unit test case framework, and generating technical specifications, outputting a complete, ready-to-use project. The system includes an AIModelManager module, a CodeGenerator module, a ProjectManager module, and a Flask Web service module; The AIModelManager module, as the core controller for multi-model game, is responsible for managing multiple Alibaba Cloud large model instances, coordinating the game process between models, and implementing the scoring mechanism and optimal selection algorithm. The CodeGenerator module, as a code generation template engine, provides a code template library for various project types. It can intelligently select the appropriate template according to user needs and generate code structures that conform to industry standards. The ProjectManager module focuses on the full lifecycle management of project files, including creating and maintaining a standard project directory structure, implementing ZIP file packaging functionality, and providing project verification and statistical analysis functions. The Flask Web Services module builds the user interface, implements the RESTful API interface specification, supports real-time code generation and download, and provides system status monitoring capabilities.

[0007] Furthermore, the system is also equipped with an intelligent project manager, which is responsible for the automated initialization of the project directory, the intelligent collection of resource files, the cleanup of redundant intermediate products, and the generation of the final deployable compressed package.

[0008] A method for generating intelligent code based on a multi-model game theory algorithm, characterized by the following steps: S1: Environment Deployment: Ensure the deployment environment meets the requirements of Python 3.7 or above, and complete the installation and configuration of all necessary dependent libraries; perform access authentication and connectivity testing on the AI ​​model service; S2: Service Startup and Initialization: Start the core service components of the system, complete the initialization test of each functional module, establish the service listening port, and prepare to receive user requests; S3: Code Upload and Inspection: Users submit project requirements through a standardized interface. The system processes and parses the requirements in a standardized manner, generates a unique task identifier, and allocates processing resources. S4: Intelligent code generation: The multi-model collaborative working mechanism is activated, and each AI model independently completes the code generation task. The system performs a preliminary quality screening of the generated results. S5: Code Evaluation and Optimization: Initiate a multi-round cross-evaluation mechanism, adopt a multi-dimensional quality evaluation system, and complete the selection and integration of the best solutions; S6: Project Building and Output: Automatically generates standardized project structures, improves project documentation and explanatory materials, and provides multiple output format options; S7: Testing, Verification and Deployment: Provides local testing and verification solutions, supports multiple deployment environment configurations, and ensures the integrity and availability of outputs; S8: System Maintenance and Expansion: Regular maintenance and update mechanism, supports flexible expansion of functional modules, and provides continuous optimization and upgrade solutions.

[0009] The advantages and beneficial effects of the invention are as follows: 1. In terms of code quality, the code, filtered and optimized through multi-model game theory, has achieved a qualitative leap in key indicators: In terms of readability, the self-explanatory ability of the code has been significantly improved through mutual verification of naming conventions and comment completeness among models. For example, when generating data processing tools, variable names and detailed function comments that conform to PEP8 standards are automatically added; In terms of execution efficiency, the cross-evaluation of algorithm complexity and resource consumption by the model effectively avoids inefficient implementations. For example, in the selection of sorting algorithms, the scheme with better time complexity is given priority; In terms of robustness, it is particularly outstanding. The system can actively inject exception handling mechanisms and input validation logic. For example, when processing user-uploaded files, it automatically generates processing branches for edge cases such as empty files, incorrect formats, and excessively large files, which fundamentally avoids program crashes caused by input anomalies and greatly reduces the cost of later debugging. In terms of development efficiency, the system has achieved a revolutionary leap in efficiency: in the traditional model, building a complete project that includes basic functions, documentation, and a testing framework usually requires hours to days of manual coding, while this system can generate a project of the same quality in minutes; for enterprise users, this technology empowers rapid technical solution verification capabilities, and R&D teams can generate prototype code for multiple technical paths simultaneously (such as comparing different database selections or architectural styles), conduct performance stress testing and functional comparison in a real environment, compress the product iteration cycle from weeks to days, and greatly accelerate the innovation implementation process.

[0010] 2. In terms of technology accessibility, the system significantly lowers the entry barrier for software development: non-professional developers (such as product managers in business departments or students in the education field) only need to describe functional requirements in natural language to obtain a complete project package that can be directly deployed and run, without needing to master programming language details or project configuration knowledge; in educational scenarios, students can shift their learning focus from grammar memorization to algorithmic thinking and system design, and teachers can quickly generate teaching case code, greatly improving teaching efficiency. For example, if a student inputs "implement a simple login interface," the system will output a complete project including front-end HTML / CSS and back-end validation logic; in addition, the system demonstrates excellent scalability and engineering compatibility: its modular architecture supports seamless integration with new AI models (such as more advanced large language models to be released in the future) or domain-specific code template libraries, and can flexibly adapt to mainstream programming languages ​​such as Java, C++, and Python, as well as the needs of vertical fields such as IoT and blockchain; the generated code strictly follows industry coding standards (such as Google Java). Style ensures seamless integration with existing development toolchains (including version control system Git, continuous integration platform Jenkins, and containerization tool Docker), avoiding collaboration barriers caused by differences in coding styles. More importantly, by establishing an objective quantitative evaluation system (such as code structure analysis based on AST and unit test coverage indicators), the system transforms the originally subjective code quality judgment into measurable and traceable technical parameters, providing an industrial-grade solution for automated code review.

[0011] 3. Overall, this system not only addresses the core pain points of existing technologies, such as unstable code quality, incomplete project structure, and low evaluation efficiency, but also redefines the human-machine collaborative software development paradigm—developers transform from code executors to requirement definers and outcome decision-makers, while AI assumes the responsibility of large-scale production of high-quality code. At a critical stage of the intelligent evolution of software engineering, it provides a systematic solution for improving development efficiency, ensuring code quality, and lowering technical barriers. Its application will profoundly impact the restructuring of software development processes and the leap in innovation efficiency, possessing broad technological prospects and industrial value. With the continuous breakthroughs in large-scale model technology and the deepening development of multi-agent collaborative theory, the system architecture also has the potential to extend to more complex software engineering tasks (such as system architecture design and legacy system refactoring), laying a key technological foundation for the next generation of AI-driven software development infrastructure and promoting the software industry towards a higher level of automation and intelligence. Attached Figure Description

[0012] Figure 1 This is a diagram of the architecture of the present invention; Figure 2 This is a diagram illustrating the technology stack and deployment architecture of the present invention; Figure 3This is a schematic diagram of the program operation of the present invention; Figure 4 This is a flowchart of the multi-model game algorithm of the present invention; Figure 5 This is a schematic diagram of the multi-model game algorithm of the present invention. Detailed Implementation

[0013] The present invention will be further described in detail below through specific embodiments. The following embodiments are merely descriptive and not limiting, and should not be used to limit the scope of protection of the present invention.

[0014] like Figures 1-5 As shown, an intelligent code generation system based on a multi-model game theory algorithm is innovative in the following aspects: 1. System Architecture The specific implementation of this invention adopts a modular architecture design, and realizes intelligent code generation through a multi-model collaborative game mechanism. The overall system architecture consists of four core modules: AIModelManager module, CodeGenerator module, ProjectManager module, and Flask Web service module.

[0015] The AIModelManager module, as the core controller for multi-model game, is responsible for managing multiple Alibaba Cloud large model instances, coordinating the game process between models, and implementing the scoring mechanism and optimal selection algorithm. The CodeGenerator module, as a code generation template engine, provides a code template library for various project types. It can intelligently select the appropriate template according to user needs and generate code structures that conform to industry standards. The ProjectManager module focuses on the full lifecycle management of project files, including creating and maintaining a standard project directory structure, implementing ZIP file packaging functionality, and providing project verification and statistical analysis functions. The Flask Web Services module builds the user interface, implements the RESTful API interface specification, supports real-time code generation and download, and provides system status monitoring capabilities.

[0016] In terms of core class design, the system adopts an object-oriented programming paradigm to build an extensible model framework. The AIModel base class defines a standardized model interface specification, including basic attributes such as model name, API address, and authentication key, as well as core methods for generating code and evaluating code quality. Based on this, the AlibabaCloudAIModel class inherits from the AIModel base class and implements the specific functions of Alibaba Cloud's large model. It manages the exclusive parameters of different models through configuration dictionaries, including key information such as application ID, model name, and API endpoint, to ensure the accuracy and security of model calls.

[0017] 2. Multi-model algorithm The multi-model game theory algorithm, the core innovation of this invention, comprises three key stages in its implementation: First, in the parallel code generation stage, the system utilizes thread pool technology to simultaneously invoke multiple AI models. Each model independently generates a code solution based on the same requirement description, and the system monitors the execution status of each model in real time and records the generation results. Next, in the cross-evaluation stage, each model takes on a different role to evaluate the quality of the code generated by other models. The evaluation process is based on a preset multi-dimensional indicator system, including dimensions such as code quality, readability, robustness, and requirement matching. The system automatically calculates a weighted average score as the final evaluation result. Finally, in the optimal code selection stage, the system selects the best code solution based on the comprehensive score and initiates the project structure completion process. To ensure code generation quality, the system implements a meticulously designed prompting engineering technique: In the code generation stage, the system constructs professional prompt templates, clearly specifying technology stack requirements (including version constraints for more than 10 key libraries such as PyQt5, pandas, and numpy), code standards (requiring complete error handling mechanisms, detailed Chinese comments, and PEP 8 coding standards), and output format restrictions; In the code evaluation stage, the system simulates the role of a professional code review expert, designs structured evaluation prompt templates, requires the model to quantitatively score from four dimensions: code quality, readability, robustness, and requirement matching, and provides a standardized output format for scoring reasons to ensure the objectivity and consistency of the evaluation results.

[0018] 3. Project document generation The project documentation generation and implementation employs a systematic approach to construct a complete and deployable project structure. The system first creates the main program file `main.py`, containing the core code for generating the optimal model. Then, it automatically generates the data processing tool module `function1.py`, which includes standardized functions such as data cleaning and statistical analysis, implementing core algorithms for data validation, cleaning, and processing, and supporting abnormal input detection and error handling. Simultaneously, it generates the file operation tool module `function2.py`, providing auxiliary functions such as file reading and writing, and format conversion. The system also automatically generates the dependency configuration file `requirements.txt`, precisely listing all libraries required by the project and their version requirements. Finally, it creates the project documentation file `manual.md`, integrating key information such as requirement descriptions, model scoring results, and user guidelines. In the implementation of the auxiliary function modules, the system adopts object-oriented design principles, encapsulating data processing functions into a reusable set of functions, including core methods such as `process_data` (calculating statistical indicators), `validate_input` (validating data validity), and `clean_data` (cleaning invalid data), equipped with comprehensive type annotations and docstrings to ensure code maintainability and readability, while providing built-in test logic to verify the correctness of the module's functions.

[0019] 4. Web Interface System Setup The web interface system is built on the Flask framework, creating an intuitive and easy-to-use user interaction environment. During system initialization, core components, including the AI ​​model manager, code generator, and project manager, are loaded to construct a complete application context. In terms of routing design, the system implements a homepage access interface, returning a rendered HTML page; the core code generation API interface receives JSON-formatted request data, verifies the validity of the project name and requirement description, creates a dedicated project directory, and triggers a multi-model game generation process. Upon successful generation, it packages the code into a ZIP file and provides a download link; the system also implements a project file download interface, providing the generated project package through a secure file transfer mechanism; the status query interface returns the system's running status in real time, including a list of available models, project statistics, and system health indicators. All API interfaces implement a strict exception handling mechanism, returning structured error messages and corresponding HTTP status codes for various exceptions such as parameter validation failures, file non-existence, and internal server errors, ensuring the reliability and predictability of API calls.

[0020] 5. Error handling and fault tolerance mechanisms The system implements a multi-layered error handling and fault tolerance mechanism, significantly improving overall stability. At the code generation level, a backup generation scheme is designed. When the main model generation fails, backup logic is automatically triggered to generate simplified code containing the basic functional framework. Although the confidence level is lower, basic usability is ensured. At the API call level, the system implements a network request strategy with a retry mechanism. For common issues such as request frequency limits, timeouts, and network anomalies, an exponential backoff algorithm is used for intelligent retries. The maximum number of retries is configurable, and a clear exception is thrown after the limit is reached. At the concurrency processing level, the system uses thread pool technology to optimize resource utilization. By setting the maximum number of worker threads and execution timeout thresholds, concurrency performance and system stability are balanced, avoiding resource exhaustion. The system also introduces a local thread storage mechanism to ensure thread safety and prevent data race issues in a multi-threaded environment. Furthermore, the system implements a cache management mechanism. A unique identifier for the requirement description is generated using the MD5 hash algorithm as the cache key. Historical generation results are serialized and stored. When encountering the same or similar requirements, the cached result is directly returned, significantly reducing redundant calculations and improving response speed. The cache manager automatically creates a cache directory, enables the retrieval and storage of cached results, and handles possible serialization exceptions to ensure the reliability of cache operations.

[0021] 6. Performance Optimization and Deployment In terms of performance optimization and deployment, the system employs multiple technologies to improve operational efficiency. Concurrency optimization utilizes a dynamic thread pool management strategy, automatically adjusting the number of worker threads based on system load to ensure response speed while avoiding resource overload. The caching mechanism extends beyond code generation results to model evaluation, establishing a rapid response channel for common demand patterns. The system supports containerized deployment, providing standard Dockerfile configurations to simplify environment dependencies. In cloud-native deployment scenarios, the system can generate complete Helm Charts and Kubernetes deployment manifests, achieving a highly available architecture and automatic scaling capabilities. For security, the system implements HTTPS encrypted communication to ensure data transmission security, restricts function access scope through RBAC access control mechanisms, regularly rotates API keys to reduce the risk of leakage, and encrypts and stores sensitive configuration information. A performance monitoring system tracks key metrics in real time, including code generation response time (within 3 minutes for typical needs), concurrent processing capacity (supporting more than 5 tasks per minute), and system resource utilization (CPU below 80%, memory below 70%), ensuring stable and reliable service. The quality assurance mechanism includes an automated code review process, regression testing suites, and a multi-dimensional monitoring system. It continuously tracks core metrics such as generation success rate, user satisfaction, and average repair time, providing data support for system iteration and optimization. The disaster recovery solution encompasses daily database backups, failover cluster configuration, and a robust rollback mechanism. Through version snapshots, configuration archives, and model backups, it ensures the system's rapid recovery capabilities under various abnormal scenarios, comprehensively guaranteeing service continuity and data security.

[0022] The specific operation procedure of this system is as follows: Step S1: Environment Deployment. Ensure the deployment environment meets the requirements of Python 3.7 or higher, and complete the installation and configuration of all necessary dependent libraries. Perform access authentication and connectivity testing on AI model services.

[0023] Step S2: Service Startup and Initialization. Start the core system service components, complete the initialization checks of each functional module, establish a service listening port, and prepare to receive user requests.

[0024] Step S3: Code Upload and Inspection. Users submit project requirements through a standardized interface. The system processes and parses the requirements, generates a unique task identifier, and allocates processing resources.

[0025] Step S4: Intelligent Code Generation. The multi-model collaborative working mechanism is activated, and each AI model independently completes the code generation task. The system performs a preliminary quality screening of the generated results.

[0026] Step S5: Code Evaluation and Optimization. Initiate a multi-round cross-evaluation mechanism, employing a multi-dimensional quality evaluation system to select and integrate the optimal solution.

[0027] Step S6: Project Construction and Output. Automatically generates a standardized project structure, completes project documentation and explanatory materials, and provides multiple output format options.

[0028] Step S7: Testing, Verification, and Deployment. A local testing and verification solution is provided, supporting various deployment environment configurations to ensure the integrity and availability of the output.

[0029] Step S8: System Maintenance and Expansion. A regular maintenance and update mechanism supports flexible expansion of functional modules and provides continuous optimization and upgrade solutions.

[0030] Although embodiments and drawings of the present invention have been disclosed for illustrative purposes, those skilled in the art will understand that various substitutions, variations and modifications are possible without departing from the spirit and scope of the present invention and the appended claims. Therefore, the scope of the present invention is not limited to the contents disclosed in the embodiments and drawings.

Claims

1. An intelligent code generation system based on a multi-model game theory algorithm, characterized in that: The system integrates multiple high-performance heterogeneous AI models through an AI model manager. When a user describes the development requirements in natural language, the system starts a multi-model game algorithm. In the initial generation phase, all connected models independently produce code solutions based on the same requirements. Then, the evaluation and game phase begins, where each model switches roles and cross-scores the code they generate based on a preset multi-dimensional quality index system. Finally, in the optimization and integration phase, the system intelligently selects the optimal code snippet based on the comprehensive score matrix generated by the game and automatically performs project structure completion operations—including generating a directory hierarchy that conforms to industry standards, adding necessary dependency declaration files, inserting standardized documentation comments, building a unit test case framework, and generating technical specifications, outputting a complete project that is ready to use out of the box. The system includes an AIModelManager module, a CodeGenerator module, a ProjectManager module, and a Flask Web service module; The AIModelManager module, as the core controller for multi-model game, is responsible for managing multiple Alibaba Cloud large model instances, coordinating the game process between models, and implementing the scoring mechanism and optimal selection algorithm. The CodeGenerator module, as a code generation template engine, provides a code template library for various project types. It can intelligently select the appropriate template according to user needs and generate code structures that conform to industry standards. The ProjectManager module focuses on the full lifecycle management of project files, including creating and maintaining a standard project directory structure, implementing ZIP file packaging functionality, and providing project verification and statistical analysis functions. The Flask Web Services module builds the user interface, implements the RESTful API interface specification, supports real-time code generation and download, and provides system status monitoring capabilities.

2. The intelligent code generation system based on a multi-model game theory algorithm according to claim 1, characterized in that: The system is also equipped with an intelligent project manager, which is responsible for the automated initialization of the project directory, the intelligent collection of resource files, the cleanup of redundant intermediate products, and the generation of the final deployable compressed package.

3. A method for intelligent code generation based on a multi-model game theory algorithm, characterized in that: Based on the generation system as described in claim 1 or 2, the method comprises the following steps: S1: Environment Deployment: Ensure the deployment environment meets the requirements of Python 3.7 or above, and complete the installation and configuration of all necessary dependent libraries; perform access authentication and connectivity testing on the AI ​​model service; S2: Service Startup and Initialization: Start the core service components of the system, complete the initialization test of each functional module, establish the service listening port, and prepare to receive user requests; S3: Code Upload and Inspection: Users submit project requirements through a standardized interface. The system processes and parses the requirements in a standardized manner, generates a unique task identifier, and allocates processing resources. S4: Intelligent code generation: The multi-model collaborative working mechanism is activated, and each AI model independently completes the code generation task. The system performs a preliminary quality screening of the generated results. S5: Code Evaluation and Optimization: Initiate a multi-round cross-evaluation mechanism, adopt a multi-dimensional quality evaluation system, and complete the selection and integration of the best solutions; S6: Project Building and Output: Automatically generates standardized project structures, improves project documentation and explanatory materials, and provides multiple output format options; S7: Testing, Verification and Deployment: Provides local testing and verification solutions, supports multiple deployment environment configurations, and ensures the integrity and availability of outputs; S8: System Maintenance and Expansion: Regular maintenance and update mechanism, supports flexible expansion of functional modules, and provides continuous optimization and upgrade solutions.

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