Automated migration system based on static analysis and large model collaborative optimization

Abstract

An automated migration system based on static analysis and large model co-optimization includes: a source code indexing module, a static analysis module, a performance analysis module, a vector database module, a RAG code generation module, and an automated testing module connected in sequence. By combining the accuracy of static analysis tools with the semantic understanding and generation capabilities of large language models, this invention can significantly improve migration efficiency and ensure consistency in code semantics and performance.

Description

Technical Field

[0001] This invention relates to a technology in the field of large language models, specifically an automated migration system based on static analysis and large model collaborative optimization. Background Technology

[0002] Existing compiler-based static analysis methods rely on Abstract Syntax Trees (ASTs) and static rules for code migration, performing well in parsing template structures and accurately locating container instances. However, these methods still have shortcomings in practical applications. First, developing customized tools requires familiarity with the underlying compiler toolchain, especially since building migration rules and adapting to specific scenarios is very complex, time-consuming, and demanding on developers. Furthermore, static analysis tools cannot perceive the dynamic behavior of code, such as container resizing strategies, iterator invalidation rules, and concurrent access, which may lead to semantic or performance inconsistencies between the migrated code and the original code. For conditional compilation and macro substitution scenarios, the limitations of static analysis tools are also significant; they cannot comprehensively parse all code paths, easily missing logical branches or generating incomplete migration code. Summary of the Invention

[0003] This invention addresses the problems of existing static analysis tools, such as complex customization, difficulty in capturing dynamic behavior, insufficient consistency of migration semantics, and inadequate performance guarantees, as well as the limitations of large language models in context understanding, complex API processing, and the accuracy of generated code. It proposes an automated migration system based on the collaborative optimization of static analysis and large models. By combining the accuracy of static analysis tools with the semantic understanding and generation capabilities of large language models, it can significantly improve migration efficiency and ensure consistency of code semantics and performance.

[0004] This invention is achieved through the following technical solution:

[0005] This invention relates to an automated migration system based on static analysis and large-scale model co-optimization, comprising: a source code indexing module, a static analysis module, a performance analysis module, a vector database module, a RAG code generation module, and an automated testing module, connected in sequence. Specifically: the source code indexing module uses the intercept-build tool to capture compilation options and build environment information, performs compilation parameter parsing, and obtains a JSON file supporting Abstract Syntax Tree (AST) parsing; the static analysis module, based on the target code parsed by a customized LibTooling tool, performs container instance and call point identification, obtaining the template parameters, definition location, and context data of the container instance; the performance analysis module, based on runtime performance data and a call point symbol list captured by the performance analysis tool, performs performance hotspot filtering, obtaining a performance benchmark report related to container operations; the vector database module, based on the target library's code and documentation information, performs container interface and semantic vectorization, obtaining a target container semantic vector database that supports retrieval; the RAG code generation module, based on the semantic vector database and context description, performs retrieval enhancement generation, obtaining container replacement code and patch files; and the automated testing module, based on preset test scripts and a Docker environment, performs functional verification and performance testing, obtaining a report on the correctness of the replacement code and performance changes. Technical effect

[0006] This invention deeply integrates Large Language Model (LLM) with static analysis techniques to achieve automated generation and verification of intelligent container replacements during code migration. By combining static analysis with the LLM, the entire code migration process is automated, including container location, replacement code generation, patch file generation, and performance verification, significantly improving migration efficiency. Semantic vector databases and Retrieval Enhanced Generation (RAG) methods ensure semantic consistency and accurate adaptation of the replacement code. Performance data capture and analysis guide replacement code generation and verify performance optimization effects, guaranteeing the performance reliability of the migrated code. The system supports dynamically expanding the target container library and semantic model, possessing extremely high scalability and adaptability. Simultaneously, by generating annotated standardized patch files, it provides developers with clear and interpretable migration results, improving code readability and maintainability. Attached Figure Description

[0007] Figure 1 This is a schematic diagram of the system of the present invention;

[0008] Figure 2 This is a flowchart of an example implementation. Detailed Implementation

[0009] like Figure 1As shown in this embodiment, an automated migration system based on static analysis and large-scale model co-optimization includes: a source code indexing module, a static analysis module, a performance analysis module, a vector database module, a RAG code generation module, and an automated testing module. Specifically: the source code indexing module uses the intercept-build tool to capture compilation options and build environment information, performs compilation parameter parsing, and obtains a JSON file supporting Abstract Syntax Tree (AST) parsing; the static analysis module uses a customized LibTooling tool to parse the target code, performs container instance and call point identification, and obtains the container instance's template parameters, definition location, and context data; the performance analysis module uses runtime performance data and a call point symbol list captured by the performance analysis tool to perform performance hotspot filtering, and obtains a performance benchmark report related to container operations; the vector database module uses the target library's code and documentation information to perform container interface and semantic vectorization, and obtains a target container semantic vector database that supports retrieval; the RAG code generation module uses the semantic vector database and context description to perform retrieval enhancement generation, and obtains container replacement code and patch files; the automated testing module uses preset test scripts and a Docker environment to perform functional verification and performance testing, and obtains a report on the correctness of the replacement code and performance changes.

[0010] The static analysis module includes a container localization unit, a container analysis unit, a container classification unit, and a macro processing unit. Specifically: the container localization unit locates the target container instance and identifies the call point based on the abstract syntax tree (AST) and compilation options of the target code, extracting the container instance's template parameters, definition location, and context data. The container analysis unit further identifies container usage in the code, including initialization, element insertion, deletion operations, and container access patterns, obtaining operation type and data access pattern information. The container classification unit classifies the identified container instances and call points, identifies the container types that need to be migrated, and determines the adaptation strategy. The macro processing unit processes macro definitions and conditional compilation, ensuring correct identification of the target container in complex code paths and guaranteeing data integrity and accuracy.

[0011] The performance analysis module includes a performance data capture unit, a performance filtering unit, a performance comparison unit, and a performance optimization suggestion unit. Specifically: the performance data capture unit uses performance analysis tools (such as Linuxperf, Intel VTune, etc.) to capture runtime performance data of the target program and generate detailed performance reports, including metrics such as execution time, memory usage, and cache hit rate. The performance filtering unit, combined with the call point symbol list generated by the static analysis module, filters out performance hotspots related to container operations and obtains performance benchmark data. The performance comparison unit compares performance data before and after replacement to perform performance difference analysis, ensuring that the replacement code is performance-comparable to the original code and providing data support for performance optimization. The performance optimization suggestion unit generates performance optimization suggestions for the container replacement code based on the performance difference analysis results, thereby guiding subsequent code generation and adjustments.

[0012] The target container vector database module comprises a data extraction unit, a semantic vectorization unit, a data storage unit, and a query and retrieval unit. Specifically: the data extraction unit uses Clang's Libtooling static analysis tool to parse the target library's source code, extracting container interface definitions, template parameters, and contextual information, while also parsing interface descriptions, usage scenarios, and performance characteristics from the developer documentation; the semantic vectorization unit uses natural language processing models (such as CodeBERT) to generate high-dimensional semantic vectors, combining code and documentation into a unified vectorized data representation; the data storage unit uses Milvus as the backend to store the generated semantic vectors and related structured data, supporting efficient vector indexing and retrieval operations; and the query and retrieval unit provides semantic retrieval and precise field matching retrieval, allowing developers to generate alternative suggestions or analyze the applicability of the target library based on the retrieval results. Through modular design and a dynamic update mechanism, the database can quickly adapt to new target libraries, support extended vectorization models and semantic rules, and provide accurate, efficient, and flexible contextual support for container replacement.

[0013] The RAG code generation module includes a semantic retrieval unit, a context enhancement unit, a code generation unit, and a code correction unit. Specifically: the semantic retrieval unit retrieves semantic vectors matching the target container interface from a semantic vector database and obtains relevant alternative code templates. The context enhancement unit, based on the retrieval results and context description, provides the Large Language Model (LLM) with the target container's usage requirements, performance requirements, and migration background through a multi-level Prompt mechanism, performing context enhancement processing. The code generation unit uses the LLM to generate candidate alternative code and performs semantic consistency checks on the generated code to ensure it conforms to the target container's usage conventions and performance requirements. The code correction unit automatically corrects the generated alternative code, eliminating potential semantic inconsistencies and performance issues, and finally outputs compliant container alternative code and patch files.

[0014] The automated testing module includes a functional verification unit, an integration testing unit, a performance regression testing unit, and a test report generation unit. Specifically: The functional verification unit performs automated unit testing based on preset test scripts and target application scenarios to verify the functional correctness of the replacement code and ensure that basic functions such as container insertion, deletion, and access operations are normal. The integration testing unit deploys the target code and replacement code in a Docker container and performs integration testing to ensure compatibility between the replacement code and the original code and to verify overall performance. The performance regression testing unit performs performance regression testing using performance benchmark data and test scripts to capture the performance data of the replacement code under actual load, ensuring that it meets optimization expectations. The test report generation unit generates a detailed test report based on the results of the functional verification and performance regression tests, including the correctness of the replacement code, performance changes, and optimization suggestions, providing a reference for developers.

[0015] like Figure 2 As shown, this embodiment illustrates the automated migration method based on the aforementioned system, which includes:

[0016] Step 1: Capturing compilation options and initializing the build environment, specifically including:

[0017] 1.1 The intercept-build tool in Clang is used to capture the compilation options and build environment information of the target code. Specifically, intercept-build intercepts all compilation commands during the code build process and generates a JSON file (such as compile_commands.json) containing complete compilation options.

[0018] 1.2 By capturing build environment information, analysis errors caused by compiler setting mismatches can be avoided, while ensuring that the output code of the migration tool is consistent with the actual environment of the target project.

[0019] Step 2: For the target library (such as Abseil Containers), construct an extensible target container vector database module by combining code parsing, documentation parsing, manual verification, and semantic vectorization. This includes:

[0020] 2.1 Perform code analysis, using static analysis tools to perform in-depth scanning of the source code, and extract key information such as various data structures, algorithm implementations, and interface definitions;

[0021] 2.2 Through document parsing, information from the library's usage documentation, API documentation, and developer guide is automatically extracted to provide a comprehensive understanding of the library's functionality and usage.

[0022] 2.3 Perform manual verification. By manually checking and verifying the results of code parsing and document parsing, ensure the accuracy and consistency of the data and avoid errors or omissions that may be caused by automated parsing.

[0023] 2.4 Semantic vectorization technology is employed to convert the extracted code and document information into semantic vector representations, enabling efficient retrieval and similarity comparison in the vector space. The combination of these methods effectively enhances the understanding depth of the target library, constructing an efficient and scalable container vector database.

[0024] Step 3: Use Clang's LibTooling framework to build a static analysis tool to locate the container instances that need to be replaced in the target code and their call points, and extract relevant context information.

[0025] The static analysis tool described above leverages Clang's Abstract Syntax Tree (AST) functionality. It identifies standard template library container instances (such as `std::vector` and `std::map`) used in the code through traversal and custom rules, parsing their template parameters and definition locations. Simultaneously, the tool captures the call points and context of each container, including the function signature, operation type, and related algorithm calls or iterator usage. This tool automates the extraction of container instance and context information, providing accurate input data for subsequent code replacement suggestion generation and semantic verification, making it suitable for large-scale codebase migration tasks.

[0026] Step 4: Use performance analysis tools (such as Linux perf or Intel VTune) to capture performance data of the target program during runtime, and combine this data with the call point symbol list generated in the static analysis to filter performance information related to the container. Specifically, this includes:

[0027] 4.1 Capture global performance data of the program by running perfrecord -g, including complete call chain information.

[0028] 4.2 Using the list of call point symbols generated in step 3) (including container method name, file name and line number), combined with the output of the automated script parsing perfscript, the symbols of the call points and their contexts are matched one by one to filter out the corresponding performance data, such as CPU time, memory allocation and cache hit rate.

[0029] 4.3 To accurately distinguish between calls to the same method in different locations, the script compares filenames and line numbers, and groups the filtering results by call point and stores them in a structured report.

[0030] Step 5: Use the Retrieval Enhanced Generation (RAG) method to generate container alternative code suggestions. This involves retrieving the target container interface and its usage description that are most similar to the semantic vectors and contextual descriptions from a vector database, forming the basis for generating alternative code. Based on the search results, a multi-level prompt is designed, taking container definitions, contextual information, performance benchmarks, etc., as input to guide the LLM in generating alternative code. The generated code suggestions include the usage of the target container, interface adaptation logic, and related considerations, ensuring semantic consistency and performance compatibility of the alternative code.

[0031] Step 6: Developer Confirmation and Code Replacement: By generating a standard patch file (Diff format), the differences in the replacement content are displayed. Developers can visually review and confirm the replacement recommendations using command-line tools. The patch file contains line-level comparison information of the original and replacement code, along with comments indicating the reasons for the replacement, the context, and performance recommendations, helping developers understand the intent behind the code changes. Developers can use common command-line tools to view the patch content or directly apply the patch file using the `patch` command to merge the replacement content into the project. Before application, developers can manually edit the patch file to adapt to project requirements and flexibly adjust the generated replacement code.

[0032] Step 7: Automated Testing and Post-Replacement Performance Verification: Run automated tests in a pre-defined Docker container to verify the functional correctness and performance changes of the replacement code. Using a consistent build environment and test scripts, automatically execute unit and integration tests to ensure the replacement code performs correctly in different test scenarios. During the performance verification phase, re-test the performance of the container after replacement using the perf tool, collect key performance indicators (such as time overhead, memory usage, and scaling behavior), compare them with the performance benchmark data before replacement, and generate a performance analysis report.

[0033] Through specific practical experiments, the above system was run using various large model APIs in a single-node server environment with an Intel Xeon Gold 5317 CPU and Ubuntu 22.04 operating system. As shown in Table 1, this method demonstrates significant advantages in both accuracy and performance.

[0034] Table 1 STL Container Migration Test

[0035] Compared with existing technologies, this method combines the code context information extracted from static analysis with the semantic vector database of the target container through semantic vectorization and retrieval-enhanced generation (RAG) methods. It designs a multi-level Prompt mechanism to guide LLM to generate alternative code that meets the requirements of semantic consistency and performance optimization. It can not only automatically generate alternative code adapted to the target environment, but also verify the correctness and performance of the code by generating standardized patch files and automated testing tools, which significantly improves the intelligence and efficiency of code migration in complex scenarios.

[0036] Through the above-mentioned innovative methods, this invention significantly improves the semantic consistency and performance compatibility of migration code. It not only overcomes the lack of applicability of existing technologies in complex scenarios, but also achieves high efficiency and flexibility in automated migration processes, with a performance improvement of up to 20%, far exceeding existing technologies in terms of accuracy and operational efficiency.

[0037] The above-described specific implementations can be partially adjusted by those skilled in the art in different ways without departing from the principles and purpose of the present invention. The scope of protection of the present invention is defined by the claims and is not limited to the above-described specific implementations. All implementation schemes within the scope of the claims are bound by the present invention.

Claims

1. An automated migration system based on static analysis and large model collaborative optimization, characterized in that, include: The system consists of four interconnected modules: source code indexing, static analysis, performance analysis, vector database, RAG code generation, and automated testing. Specifically: the source code indexing module uses the intercept-build tool to capture compilation options and build environment information, performs compilation parameter parsing, and obtains a JSON file that supports abstract syntax tree (AST) parsing; the static analysis module uses a customized LibTooling tool to parse the target code, identifies container instances and call points, and obtains the template parameters, definition locations, and context data of the container instances; the performance analysis module uses runtime performance data and a list of call point symbols captured by the performance analysis tool to perform performance hotspot filtering and obtain a performance benchmark report related to container operations; the vector database module uses the target library's code and documentation information to perform container interface and semantic vectorization, obtaining a retrieval-enabled semantic vector database for the target containers; the RAG code generation module uses the semantic vector database and context descriptions to perform retrieval enhancement generation, obtaining container replacement code and patch files; and the automated testing module uses preset test scripts and a Docker environment to perform functional verification and performance testing, obtaining reports on the correctness of the replacement code and performance changes.

2. The system of claim 1, wherein, The static analysis module includes a container location unit, a container analysis unit, a container classification unit, and a macro processing unit. Specifically: the container location unit locates the target container instance and identifies the call point based on the abstract syntax tree and compilation option information of the target code, extracting the container instance's template parameters, definition location, and context data; the container analysis unit further identifies the container usage in the code, including initialization, element insertion, deletion operations, and container access modes, obtaining operation type and data access mode information; the container classification unit classifies the identified container instances and call points, identifies the container types that need to be migrated, and determines the adaptation strategy; and the macro processing unit processes macro definitions and conditional compilation, ensuring correct identification of the target container in complex code paths and guaranteeing data integrity and accuracy.

3. The system of claim 1, wherein, The performance analysis module includes a performance data capture unit, a performance filtering unit, a performance comparison unit, and a performance optimization suggestion unit. Specifically: the performance data capture unit uses performance analysis tools to capture runtime performance data of the target program and generates detailed performance reports, including execution time, memory usage, and cache hit rate metrics; the performance filtering unit combines the call point symbol list generated by the static analysis module to filter out performance hotspots related to container operations and obtain performance benchmark data; the performance comparison unit compares performance data before and after replacement to perform performance difference analysis, ensuring that the replacement code is performance-comparable to the original code and providing data support for performance optimization; and the performance optimization suggestion unit generates performance optimization suggestions for the container replacement code based on the performance difference analysis results, thereby guiding subsequent code generation and adjustments.

4. The system of claim 1, wherein, The target container vector database module includes: a data extraction unit, a semantic vectorization unit, a data storage unit, and a query and retrieval unit. Specifically: the data extraction unit parses the target library source code using static analysis tools to extract container interface definitions, template parameters, and contextual information, while also parsing interface descriptions, usage scenarios, and performance characteristics from the developer documentation; the semantic vectorization unit uses a natural language processing model to generate high-dimensional semantic vectors, combining the code and documentation to represent unified vectorized data; and the query and retrieval unit is used for semantic retrieval and precise field matching.

5. The automated migration system based on static analysis and large model co-optimization as described in claim 1, characterized in that, The RAG code generation module includes a semantic retrieval unit, a context enhancement unit, a code generation unit, and a code correction unit. Specifically: the semantic retrieval unit retrieves semantic vectors matching the target container interface from a semantic vector database and obtains relevant alternative code templates; the context enhancement unit, based on the retrieval results and context description, provides the large language model with the target container's usage requirements, performance requirements, and migration background through a multi-level Prompt mechanism, performing context enhancement processing; the code generation unit uses the large language model to generate candidate alternative codes and performs semantic consistency checks on the generated codes; and the code correction unit automatically corrects the generated alternative codes, eliminating potential semantic inconsistencies and performance issues, ultimately outputting compliant container alternative codes and patch files.

6. The automated migration system based on static analysis and large model co-optimization as described in claim 1, characterized in that, The automated testing module includes a functional verification unit, an integration testing unit, a performance regression testing unit, and a test report generation unit. Specifically, the functional verification unit performs automated unit testing based on preset test scripts and target application scenarios to verify the functional correctness of the alternative code. The integration testing unit deploys the target code and the alternative code in a Docker container, performs integration testing, and verifies the overall performance. The performance regression testing unit performs performance regression testing using performance benchmark data and test scripts, captures the performance data of the alternative code under actual load, and generates a test report.

7. An automated migration method based on the system described in any one of claims 1-6, characterized in that, include: Step 1: Capturing compilation options and initializing the build environment, specifically including: 1.1 The intercept-build tool in Clang is used to capture the compilation options and build environment information of the target code. Specifically, intercept-build intercepts all compilation commands during the code build process and generates a JSON file containing complete compilation options. 1.2 By capturing build environment information, analysis errors caused by compiler setting mismatches can be avoided, while ensuring that the output code of the migration tool is consistent with the actual environment of the target project; Step 2: Build an extensible target container vector database module for the target library, specifically including: 2.1 Perform code analysis, using static analysis tools to perform in-depth scanning of the source code, and extract various data structures, algorithm implementations, and interface definitions; 2.2 Through document parsing, automatically extract information from the library's user documentation, API documentation, and developer guide; 2.3 Perform manual verification by manually checking and verifying the results of code parsing and documentation parsing; 2.4 Semantic vectorization technology is used to convert the extracted code and document information into semantic vector representations and construct a container vector database; Step 3: Use Clang's LibTooling framework to build a static analysis tool to locate the container instances that need to be replaced in the target code and their call points, and extract relevant context information; Step 4: Use performance analysis tools to capture performance data of the target program during runtime, and combine this data with the call point symbol list generated in the static analysis to filter performance information related to the container, specifically including: 4.1 Capture global performance data of the program, including complete call chain information, by running perfrecord -g; 4.2 Using the list of call point symbols generated in step 3), combined with the output of the automated script parsing perfscript, the symbols of the call points and their contexts are matched one by one to filter out the corresponding performance data; 4.3 To accurately distinguish between calls to the same method in different locations, the script compares filenames and line numbers, and groups the filtering results by call point and stores them in a structured report; Step 5: Use the search enhancement generation method to generate container replacement code suggestions; Step 6: Developer confirmation and code replacement; Step 7: Automated testing and performance verification after replacement.

8. The automated migration method according to claim 7, characterized in that, Step 5 specifically includes: forming the basis for generating alternative code by retrieving the target container interface and its usage description that are most similar to the semantic vector and context description from the vector database; designing a multi-level Prompt based on the retrieval results to guide the LLM to generate alternative code with container definition, context information, and performance benchmarks; the generated code suggestions include the usage of the target container, interface adaptation logic, and related precautions.

9. The automated migration method according to claim 7, characterized in that, Step 6 specifically includes: generating standard patch files to demonstrate the differences in the alternative content, and using command-line tools to visually review and confirm the alternative recommendations; The replacement includes line-level comparison information of the original code and the replacement code, along with comments indicating the reasons for the replacement, the context, and performance recommendations.

10. The automated migration method according to claim 7, characterized in that, Step 7 specifically includes: running automated tests in a preset Docker container to verify the functional correctness and performance changes of the alternative code, and automatically executing unit tests and integration tests of the code through a consistent build environment and test scripts; In the performance verification phase, the perf tool is used again to test the performance of the replaced container, key performance indicators are collected and compared with the performance benchmark data before the replacement, and a performance analysis report is generated.

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