Code optimization and reconstruction system

Abstract

The invention provides a code optimization and reconstruction system which comprises a code analysis module used for analyzing the structure, behavior and operation performance of an initial source code of a program and preliminarily repairing the initial source code according to an analysis result to obtain a target source code; the demand and architecture analysis module is used for analyzing the consistency of the demand of the program and the architecture of the target source code and monitoring the demand change of the program; when inconsistency between the demand and the architecture is identified or the demand is changed, recommending a code architecture and a mode according to a consistency analysis result and the demand change; and the optimization and reconstruction module is used for analyzing potential defects of code modes, performance bottlenecks and code changes of the target source codes and generating optimization and reconstruction suggestions or automatically optimizing and reconstructing codes. According to the system, the manual code review time is shortened, and the code optimization and reconstruction efficiency is improved; manual intervention is reduced, and the code optimization and reconstruction accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the fields of artificial intelligence and machine learning, and in particular to a code optimization and reconstruction system. Background Art

[0002] In today's era of rapid development of information technology, software systems are becoming more and more complex, involving huge code bases and diverse technology stacks. As business needs continue to evolve, software systems need to be continuously iterated and updated to adapt to market changes and user needs. However, in this process, the code base may gradually accumulate technical debt, leading to problems such as code redundancy, logical confusion, and unreasonable structure. These problems not only reduce the performance of the software and increase maintenance costs, but also affect the scalability and maintainability of the software.

[0003] Traditional code optimization and refactoring methods mainly rely on the experience and skills of developers, and identify problems and improvements through manual code review. Since manual review is time-consuming, labor-intensive and subjective, code optimization and refactoring are inefficient and inaccurate. Therefore, how to optimize and refactor code efficiently and accurately becomes a problem to be solved. Summary of the invention

[0004] In order to solve the problem of low efficiency and low accuracy of code optimization and reconstruction in the prior art, the present invention provides a code optimization and reconstruction system.

[0005] In order to achieve the above object, the present invention provides the following technical solutions: A code optimization and refactoring system, comprising: A code parsing module is used to analyze the structure, behavior and running performance of the initial source code of the program through a code parser, and perform preliminary repairs on the initial source code according to the analysis results to obtain the target source code; The requirements and architecture analysis module is used to analyze the consistency between the program requirements and the target source code architecture, and monitor the changes in program requirements. When inconsistencies between requirements and architecture are identified, or requirements change, the code architecture and mode are recommended based on the consistency analysis results and requirement changes. The optimization and refactoring module is used to analyze the code patterns, performance bottlenecks and potential defects of code changes in the target source code; based on the analysis results and the code architecture and patterns recommended by the requirements and architecture analysis module, it generates optimization and refactoring suggestions or automatically optimizes and refactors the code.

[0006] Optionally, it also includes: The testing and verification module is used to automatically generate test cases and test the optimized and refactored code; A user interaction module is used to display optimization and reconstruction suggestions and optimization and reconstruction results; and receive user feedback data on the optimization and reconstruction suggestions and optimization and reconstruction results; The adaptive learning module is used to obtain user feedback data and code change data, and optimize the code optimization and refactoring strategies of the code optimization and refactoring system based on the user feedback data and code change data.

[0007] Optionally, the code parser includes a static analysis engine and a dynamic analysis engine. The static analysis engine is used to perform lexical analysis, syntax analysis, semantic analysis, data flow analysis, control flow analysis, symbolic execution and defect detection on the initial source code. The dynamic analysis engine is used to simulate the actual running environment of the program, analyze the running status, performance and security performance of the program in the simulated environment, and obtain analysis results.

[0008] Optionally, the optimization and reconstruction module includes: An unsupervised learning algorithm unit, used to identify code patterns of target source code through clustering technology; Time series analyzer, used to analyze the changing trend of the time series composed of performance data and determine the cause of performance bottlenecks; A supervised learning unit for predicting potential defects in code changes through pre-trained machine learning models.

[0009] Optionally, the optimization and reconstruction module also includes: The principal component analysis unit is used to perform dimensionality reduction processing on the high-dimensional feature space of the target source code and extract key features of the target source code.

[0010] Optionally, the testing and verification module includes: Model-Based Testing (MBT) generator, used to automatically generate test cases; A heuristic search algorithm optimizer is used to select some test cases from the test cases generated by the MBT generator through a genetic algorithm and optimize the parameter configuration of some test cases; Automatic testing framework integrator, used to execute optimized test cases and generate test reports.

[0011] Optionally, the adaptive learning module includes an agent, a reward function, and a reinforcement learning model: The intelligent agent is used to perform code refactoring operations or code optimization operations based on preset strategies; The reward function is designed based on the user's feedback data and code change data, and is used to reward the agent's code refactoring operation or code optimization operation execution results, so that the agent updates the reinforcement learning model based on the reward to optimize the performance of the code optimization and refactoring system.

[0012] Optionally, the user interaction module displays the performance comparison and impact assessment before and after the code improvement through interactive heat maps and three-dimensional models; and integrates the user's feedback data into the adaptive learning module for learning and adaptation through an intelligent feedback loop mechanism.

[0013] Optionally, the requirement and architecture analysis module analyzes the consistency between the requirements and the target source code architecture based on natural language processing technology and code parsing tools.

[0014] The code optimization and reconstruction system provided by the present invention has the following beneficial effects: The code parsing module of the code optimization and refactoring system automatically repairs syntax problems, structural problems, and performance problems in the code through a code analyzer, thereby ensuring the quality of the code. The requirements and architecture analysis module analyzes the consistency of code requirements and monitors changes in requirements. When code requirements are inconsistent or requirements change, it automatically recommends appropriate code architectures and patterns and promptly triggers the optimization and refactoring module to generate optimization and refactoring suggestions or automatically optimize and refactor the code. On the one hand, users can optimize the code based on the optimization and refactoring suggestions, reduce the time for manual code review, and improve the efficiency of code optimization and refactoring. On the other hand, the system can automatically optimize and refactor the code based on the analysis results of the code, reduce manual intervention, and objectively optimize and refactor the code based on the problems in the code itself, thereby improving the accuracy of code optimization and refactoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiment of the present invention and its design scheme, the following briefly introduces the drawings required for this embodiment. The drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A schematic diagram of the structure of a code optimization and reconstruction system provided by an embodiment of the present invention; Figure 2 A schematic diagram of the structure of another code optimization and reconstruction system provided by an embodiment of the present invention; Figure 3 A schematic diagram of the operation flow of the code optimization and reconstruction system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to enable those skilled in the art to better understand the technical solution of the present invention and implement it, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the scope of protection of the present invention.

[0018] In the description of the present invention, it is to be understood that the terms “center”, “longitudinal”, “lateral”, “length”, “width”, “thickness”, “up”, “down”, “front”, “back”, “left”, “right”, “vertical”, “horizontal”, “top”, “bottom”, “inside”, “outside”, “axial”, “radial”, “circumferential”, etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the technical solutions of the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0019] In addition, the terms "first", "second", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance. In the description of the present invention, it should be noted that, unless otherwise clearly specified or limited, the terms "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. In the description of the present invention, unless otherwise specified, "plurality" means two or more, which will not be described in detail here.

[0020] Example 1 Figure 1 A schematic diagram of the structure of a code optimization and reconstruction system provided by an embodiment of the present invention.

[0021] For example, Figure 1 As shown, the code optimization and reconstruction system 100 includes the following modules: The code parsing module 110 is used to analyze the structure, behavior and running performance of the initial source code of the program through a code parser, and perform preliminary repairs on the initial source code according to the analysis results to obtain the target source code.

[0022] The function of the code parsing module 110 is mainly to ensure the quality of the initial source code of the program, to provide more accurate data support for subsequent intelligent optimization, architecture analysis, etc., and to ensure that subsequent processing is based on accurate code understanding.

[0023] The code analyzer is a software tool for evaluating the quality, performance and security of source code. Optionally, the code analyzer includes a static analysis engine and a dynamic analysis engine.

[0024] The static analysis engine is a powerful code review tool that improves software quality through multi-stage analysis without running the program.

[0025] Dynamic Analysis Engine: is a software tool that monitors and analyzes the behavior of a program while it is running to detect runtime errors, performance issues, security vulnerabilities, and other potential problems. The dynamic analysis engine covers six core technologies: fine-grained analysis of resource usage through performance profiling to identify bottlenecks; timely detection and resolution of memory leaks and unreasonable usage through memory monitoring, such as identifying memory leaks, fragmentation and unreasonable memory usage patterns through heap dump analysis and reference count monitoring, and timely detection and location of memory management problems; integrated intrusion detection system (IDS) for security event monitoring to defend against potential threats, such as malicious code execution and unauthorized access attempts, and take immediate response measures once abnormal behavior is detected, such as logging and alarming; concurrent behavior analysis ensures execution correctness and performance in a multi-threaded environment, such as detecting concurrent problems such as deadlocks and race conditions by tracking thread activities, lock contention and resource sharing patterns; log and event tracking promotes understanding of the execution process and rapid location of problems; and real-time capture of exceptions and faults to assist in accurate diagnosis, such as capturing program crashes and error handling paths through signal processing and exception handling frameworks, combined with stack traces, to provide key clues for problem diagnosis. This comprehensive technical system comprehensively ensures the performance optimization, security reinforcement and stability of software operation, provides developers with data-driven decision support, and accelerates the troubleshooting and performance tuning process.

[0026] Among them, static analysis mainly includes lexical analysis, syntax analysis, semantic analysis, data flow analysis, control flow analysis, symbolic execution and defect detection. It mainly checks whether the code follows the established coding standards and style guidelines; evaluates the complexity of the code, including cyclomatic complexity and method complexity; identifies code smells of design problems or potential defects, such as duplicate code, overly long functions and overly large classes; analyzes the dependencies between code components and evaluates the degree of modularization and decoupling.

[0027] The process of static analysis is as follows: Select a suitable static analysis tool (static analysis engine), such as FindBugs, according to the programming language and development requirements; download and install the selected static analysis tool, and configure it as needed; import the initial source code into the static analysis tool; run the static analysis tool to analyze the initial source code. First, lexical analysis breaks down the source code into basic words; then, syntax analysis builds an abstract syntax tree to verify that the code structure follows the language specification. Semantic analysis deeply explores the meaning of the code to ensure type correctness and correct use of variables. Data flow and control flow analysis are then carried out, the former tracking variable state changes and invalid references, and the latter revealing the logic of the program execution path. Symbolic execution further explores all possible execution paths to discover abnormal situations, while defect detection integrates various analysis results, using multiple strategies such as rule matching strategies, pattern recognition strategies, frequency of use analysis, and concurrency problem detection to identify programming errors, security vulnerabilities, and performance bottlenecks, and generate an analysis report based on the above analysis; fix the problems in the initial source code one by one according to the problems in the analysis report.

[0028] Dynamic analysis is a method of observing and evaluating the code through a dynamic analysis engine during the actual operation of the program. It mainly monitors and analyzes the program's running status, performance, and security performance to discover potential problems and defects. Specifically, the dynamic analysis process is: generate a detailed test plan through artificial intelligence based on requirements and goals; design specific test cases based on the test plan; build a test environment (simulated environment) similar to the real environment; execute the test step by step according to the test case and record the test results; analyze the test results to find out the existing problems and defects; and fix the problems in the source code based on the test results.

[0029] The tests mainly include: Unit testing: Write test cases that simulate user input and expected output.

[0030] Integration testing: Integrate different modules together for testing to ensure that there are no problems with the collaboration and interaction between modules.

[0031] Performance testing: Test the performance and response speed of a program by simulating the load and concurrency in real scenarios and monitoring the program's performance indicators, such as CPU usage, memory consumption, and response time.

[0032] Security testing: Test the security and vulnerabilities of the program by simulating attack scenarios.

[0033] In the above embodiment, the code parsing module uses both static and dynamic analysis to not only control the code quality, but also provide accurate data support for subsequent intelligent optimization and architecture analysis, ensuring that all subsequent processing is based on accurate code understanding, reducing the cost and risk of subsequent repairs.

[0034] The requirement and architecture analysis module 120 is used to analyze the consistency between the program requirements and the target source code architecture, and to monitor changes in program requirements; when inconsistencies between requirements and architecture are identified, or requirements change, code architecture and patterns are recommended based on consistency analysis results and requirement changes.

[0035] In the code optimization and refactoring system, the requirement and architecture analysis module 120 is deeply integrated with the code parsing module 110, and its main functions are to identify the consistency issues of the program's requirements and architecture, monitor requirement changes, and recommend code architecture and patterns.

[0036] Specifically, Natural Language Processing (NLP) technology is used to parse requirement documents, perform text analysis on requirement documents, apply entity recognition and relationship extraction technology to convert text into structured requirement items, provide automatic checking of requirement consistency and completeness based on software requirements and architectural changes, and implement real-time monitoring and impact analysis of requirement changes to assess the potential impact of changes on the existing architecture.

[0037] When inconsistencies between requirements and architecture are identified, or when requirements change, pre-trained machine learning algorithms are applied to automatically recommend software architecture styles and patterns based on requirements. Architectural decision support is provided to help weigh the pros and cons of different architectural choices. Automatically verify that the software architecture is consistent with requirements, ensuring traceability from requirements to design.

[0038] The optimization and reconstruction module 130 is used to analyze the code pattern, performance bottlenecks and potential defects of code changes of the target source code; based on the analysis results and the code architecture and pattern recommended by the requirements and architecture analysis module, it generates optimization and reconstruction suggestions or automatically optimizes and reconstructs the code.

[0039] The main function of the optimization and refactoring module 130 is to analyze the code patterns, performance bottlenecks and potential defects of code changes in the target source code, and based on the requirements-architecture consistency analysis results and recommended code architecture and patterns of the requirements and architecture analysis module 120, use automated refactoring rules to generate optimization refactoring suggestions or automatically optimize refactoring code.

[0040] It should be understood that automated refactoring rules should include code pattern adjustments, defect repairs, performance optimizations, and architectural improvements, etc. The generated refactoring suggestions should include the specific steps, expected effects, and potential risks of the optimized refactoring.

[0041] Specifically, the optimization and refactoring module 130 identifies and classifies the problems in the code based on the code pattern, potential defect analysis results, and performance analysis results. Common classifications include performance issues, code smells, security vulnerabilities, and insufficient test coverage. Based on the problem classification, the system generates corresponding optimization and refactoring suggestions. These suggestions may include, but are not limited to: performance optimization, code refactoring, security reinforcement, and test improvement.

[0042] Optionally, the generated suggestions are prioritized, and the factors considered in the ranking may include the severity of the problem, the difficulty of repair, the impact on the system, etc. Suggestions with higher priorities are fed back to the user first, or automatically optimized and refactored.

[0043] For some low-risk and standardized refactoring operations, such as extracting methods, renaming variables, and formatting code, the system can provide automated refactoring tools such as the IDEA development tool (full name IntelliJ IDEA) to automatically refactor the code. The optimization results can then be fed back to the code parsing module 110 to adjust the optimization strategy in real time and achieve precise optimization.

[0044] It should be understood that the “system” not specifically described in the embodiments of the present invention refers to the “code optimization and reconstruction system”.

[0045] Optionally, the optimization and reconstruction module 130 includes: The unsupervised learning algorithm unit is used to identify the code pattern of the target source code through clustering technology.

[0046] Specifically, K-means clustering technology can be used to first perform pattern recognition and style classification on the code snippets of the target code. This process can automatically discover typical patterns in the code, such as common function structures and specific programming habits, and provide a basic framework for subsequent intelligent optimization. Through clustering, the system can understand and summarize the commonalities and differences of the code, laying the foundation for the formulation of personalized optimization strategies.

[0047] Time series analyzer is used to analyze the changing trends of the time series composed of performance data and determine the causes of performance bottlenecks.

[0048] The performance data includes, but is not limited to, central processing unit (CPU) usage, memory usage, response time, and throughput.

[0049] Specifically, the above performance data is organized into a time series format through a time series analyzer, and each time point corresponds to the performance indicator value at a specific moment. The collected data is cleaned to remove outliers and missing values, and can also be normalized or standardized to ensure data quality and prepare for subsequent analysis. Useful features are extracted from time series data, which may include mean, standard deviation, maximum, minimum, peak-to-valley difference, trend, and seasonal components. Through trend analysis of time series, the changing trend of performance indicators over time can be identified.

[0050] It should be understood that an upward trend may indicate that resources are becoming increasingly scarce, while a downward trend may mean improper resource allocation or system optimization effects. Use statistical methods (such as Z-score and interquartile range) or machine learning models (such as IsolationForest and Autoencoder) to identify anomalies in performance data, which are often related to performance bottlenecks. Analyze the correlation between different performance indicators and find out whether changes in certain indicators are closely related to other performance degradations to help locate the root cause of the problem. Combined with the above analysis results, comprehensively judge and locate the specific causes of performance bottlenecks to provide a basis for optimization measures.

[0051] A supervised learning unit for predicting potential defects in code changes through pre-trained machine learning models.

[0052] After completing the time series analysis and identifying the specific causes of the performance bottleneck, you can use unsupervised or statistical learning methods (such as state-space models and long short-term memory networks) to build a time series prediction model based on historical performance data to simulate and predict possible future performance degradation trends and potential performance bottleneck time points. This step focuses on the quantitative prediction of performance indicators.

[0053] At the same time, in order to discover other potential defects in the initial code at an early stage, support vector machines (SVM) or random forest models are used for training. These models learn the relationship between code features and defects based on labeled historical defect data to predict the risk of defects in new or modified code. This step focuses on predictive analysis of defects, which complements performance prediction and provides comprehensive support for continuous optimization and maintenance of software.

[0054] In the above embodiment, natural language processing technology is used to understand and process the syntax and semantics of the programming language, and the module can self-learn and adapt based on historical defect data, user feedback and continuous code changes to improve the accuracy of suggestions.

[0055] Optionally, the optimization and reconstruction module 130 further includes a principal component analysis (PCA) unit, which is used to perform dimensionality reduction processing on the high-dimensional feature space of the target code to extract key features of the target code.

[0056] The core of PCA is to map a high-dimensional data set into a low-dimensional space while maintaining the variability of the data set as much as possible.

[0057] Through dimensionality reduction, a large number of unimportant or redundant features are eliminated, and only those principal components that can explain most of the variance in the data are retained. In the process of dimensionality reduction, PCA actually compresses the original data, discards those dimensions that contribute less to the variance, and retains only the feature directions that contribute most to data changes. This is equivalent to a lossy compression of the data, but retains the main structure and information of the data. PCA reduces the complexity of the data, making analysis more efficient, and also helps identify the factors that have the greatest impact on code quality and performance. This not only optimizes the use of storage and computing resources, but also effectively simplifies the data structure, removes redundant information, and highlights the main features of the data, making subsequent analysis more efficient and accurate.

[0058] Optionally, the optimization and refactoring module 130 may be integrated into a CI / CD (continuous integration / continuous deployment) process to ensure that the code is automatically optimized and refactored before or after being submitted to a version control system.

[0059] In the above embodiment, machine learning technology is used to analyze the initial source code to identify patterns, structures and potential problems. Through deep learning algorithms, the system can understand the logic and functions of the initial source code, automatically identify and optimize the redundant and inefficient parts of the code, and thus make optimization suggestions, including performance improvement, memory usage optimization and code structure simplification, etc. At the same time, according to software requirements and architecture changes, the code structure is intelligently reconstructed to improve code quality and operating efficiency.

[0060] At the same time, it reduces the time for manual code review and improves the maintainability and readability of the code. Through continuous learning and adaptation, it helps the team achieve a more efficient and reliable software development process.

[0061] Figure 2 A schematic diagram of the structure of another code optimization and reconstruction system provided by an embodiment of the present invention.

[0062] For example, Figure 2 As shown, the code optimization and reconstruction system 100 includes: a code parsing module 110 , a requirement and architecture analysis module 120 , an optimization and reconstruction module 130 , a testing and verification module 140 , a user interaction module 150 and an adaptive learning module 160 .

[0063] The testing and verification module 140 is used to generate test cases and test the optimized and reconstructed code.

[0064] Specifically, after the optimization and reconstruction module 130 optimizes and reconstructs the code, the testing and verification module 140 uses artificial intelligence technology to automatically generate test cases, automatically run the test cases, generate report test results, and feed back the test results to the intelligent optimization and reconstruction module 130 and the requirements and architecture analysis module 120, further closing the feedback loop and improving test coverage and accuracy.

[0065] Optionally, the testing and verification module 140 includes: MBT generator, used to generate test cases.

[0066] MBT generator is a method to automatically generate test cases using system models. In this module, MBT generator first constructs an abstract system behavior model by analyzing the software requirements specification or design model (such as Unified Modeling Language (UML) state machine, activity diagram, etc.). This model can express various operating states, state transitions, and responses to the outside world of the software.

[0067] The heuristic search algorithm optimizer is used to select some test cases from the test cases generated by the MBT generator through a genetic algorithm and optimize the parameter configuration of some test cases.

[0068] Specifically, faced with a large number of test cases generated by the MBT generator, heuristic algorithms, such as genetic algorithms or simulated annealing algorithms, can optimize the selection of test cases or parameter configuration. These algorithms find the optimal solution or near-optimal solution to the problem by simulating natural selection and evolution processes (genetic algorithms) or material annealing processes (simulated annealing algorithms).

[0069] Automatic testing framework integrator, used to execute optimized test cases and generate test reports.

[0070] Specifically, we transform optimized test cases into actual actions. Through Selenium (an automated testing tool), JUnit (a unit testing framework) and TestNG (a testing framework), we transform test cases into executable automated testing scripts, embed them into the continuous integration / continuous deployment (CI / CD) process, realize automated test execution, collect test results and generate test reports.

[0071] In the above embodiment, test cases are automatically generated to reduce the time and cost of manually writing test cases, and machine learning algorithms are applied to predict possible defects in the code, and testing is performed in advance to ensure the reliability of code changes.

[0072] The user interaction module 150 is used to display optimization and reconstruction suggestions and optimization and reconstruction results; and receive user feedback data on the optimization and reconstruction suggestions and optimization and reconstruction results.

[0073] The user interaction module 150 integrates an advanced graphical user interface that can display detailed code optimization suggestions, real-time refactoring progress and comprehensive test reports. It also uses advanced data visualization technology, such as interactive heat maps and three-dimensional model displays, to intuitively reflect the performance comparison and impact assessment before and after code improvement; it also introduces an intelligent feedback loop mechanism, allowing users to directly quantify the effects of optimization suggestions through a graphical interface, provide multi-dimensional feedback, and participate in discussions. All user feedback is integrated into the core algorithm of the system in real time to continuously optimize the accuracy and practicality of the suggestions. For optimization suggestions that need to be executed manually, provide clear guidance and steps. Evaluate the effectiveness of optimization suggestions and provide feedback. Establish a feedback loop to use user feedback to improve the system's suggestion generation algorithm and rule base. The system can further adjust the optimization strategy based on the feedback.

[0074] The user interaction module 150 also allows developers to configure interface layout, notification settings, and operation processes according to personal preferences and project requirements. This personalized setting improves the user experience and makes the interaction more efficient. Developers can give feedback on optimization suggestions and reconstruction plans directly on the user interface, such as accepting, rejecting, or proposing modifications. The system will adjust subsequent analysis and suggestions based on this feedback.

[0075] The adaptive learning module 160 is integrated into the code optimization and reconstruction system. The module mainly uses the reinforcement learning (RL) algorithm.

[0076] Optionally, the adaptive learning module 160 includes: an agent, a reward function, and a reinforcement learning model.

[0077] The intelligent agent is the main body of the adaptive learning module 160, which is responsible for sensing the external environment state, performing code refactoring operations or code optimization operations based on preset strategies, and learning according to the received feedback. The key technical points to be considered in designing the intelligent agent include: State Representation: Abstracting complex environments into a form that can be understood by intelligent agents, such as using neural networks to encode environmental states to support high-dimensional data processing.

[0078] Action Space Construction: Defines all possible actions that the agent can perform, which can include discrete actions (such as selecting a specific algorithm) or continuous actions (such as adjusting parameters).

[0079] Policy: A rule that determines which action to choose in a given state. It can be deterministic or stochastic. Optimizing the policy is the core goal of reinforcement learning.

[0080] The reward function is the driving force of the agent's learning. It is used to reward the agent for the execution results of the code refactoring or code optimization operation, so that the agent can update the reinforcement learning model based on the reward to optimize the performance of the code optimization and refactoring system. The reward function must meet the following requirements when constructing it: (1) Immediate vs Delayed Rewards: Balancing the rewards for immediate action results with the rewards for achieving long-term goals, ensuring that the agent focuses on short-term benefits while not neglecting long-term planning.

[0081] (2) Reward Function Design: Design a sophisticated reward function that can both reflect the success of the task and encourage the exploration of new strategies to avoid premature convergence to the local optimum.

[0082] (3) Sparse Reward Problem: In some complex tasks, there are few effective reward signals, and techniques such as curiosity-driven learning or potential reward shaping are needed to encourage agents to explore unknown areas.

[0083] In an embodiment of the present invention, the reward function is designed based on user feedback and the results of code changes, such as positive rewards corresponding to optimization suggestions accepted by the user.

[0084] The reinforcement learning model continuously learns to optimize the performance of the code optimization and refactoring system. The reinforcement learning model can be any of Q-learning, deep Q-network, policy gradient, Actor-Critic architecture or deep reinforcement learning model.

[0085] Q-learning and deep Q-network are value function-based methods. Q-learning directly learns the action-value function, while deep Q-network approximates this value function through a deep neural network, which is particularly suitable for processing high-dimensional state space.

[0086] Policy gradient methods such as REINFORCE and Proximal Policy Optimization optimize directly in the policy space and are suitable for problems with continuous action spaces.

[0087] Actor-Critic architecture: It combines the advantages of value function method and policy gradient, where "Actor" selects actions and "Critic" evaluates the value of the action, such as Advantage Actor-Critic (A2C) and A3C (asynchronous version), which performs well in improving learning efficiency and stability.

[0088] In the actual process, the appropriate reinforcement learning model can be selected according to the specific problem.

[0089] Next, we introduce the collaboration between the agent, reward function, and reinforcement learning model: Initialization: The agent starts observing the initial state of the code base.

[0090] During the initialization phase, the agent first imports a complete snapshot of the code base, including source code, configuration files, and related documents. The code is scanned using static code analysis tools (such as SonarQube or FindBugs) to identify code quality indicators, complexity, and potential defects. In addition, the agent extracts the code's commit history and change log from version control systems (such as Git) to build a comprehensive view of the code evolution.

[0091] Decision and Execution: The agent selects and executes the optimal reconstruction action based on the current policy and the recommendations of the reinforcement learning model.

[0092] The agent uses a defined action space in this phase, which may include code refactoring, performance optimization, and code improvement. The policy model, which is a rule-based system or a trained machine learning model, is used to evaluate which actions are most likely to lead to positive results in the current state. The reinforcement learning model guides the decision-making process based on historical data and expected rewards. The agent executes the selected optimization refactoring action.

[0093] Feedback acquisition: After performing an action, the agent obtains feedback through a reward function.

[0094] After the action is executed, the agent evaluates its effect through the reward function. The reward function calculates the reward value based on factors such as improved code quality, improved performance, and reduced maintenance costs. For example, if the refactoring operation performed improves the readability of the code and reduces defects, the agent will receive a positive reward; conversely, if new problems are introduced, a negative reward will be received.

[0095] Model update: The agent uses the feedback it receives to update the reinforcement learning model and adjust its strategy.

[0096] The agent uses the results of the actions and the reward feedback it receives to update the reinforcement learning model. This involves an experience replay mechanism, which stores the interaction data in a buffer and uses it to train and optimize the model. In this way, the agent continuously adjusts its strategy to better adapt to changes in the code base and the evolution of development needs.

[0097] Iterative learning: The agent continuously cycles through observation, decision-making, execution, feedback, and learning. Over time, through this continuous learning and adaptation process, the agent gradually improves the accuracy and effectiveness of its optimization decisions, thereby achieving continuous improvement in the quality and performance of the code base.

[0098] It should be understood that before applying the code optimization and refactoring system, the machine learning model involved in the system needs to be trained so that the machine learning model can automatically identify problems in the code. The process is as follows: Data collection and preprocessing: Collect the source code of the program, which can include multiple files and programming languages. You can use a version control system (such as Git) to obtain historical submissions and code change records, including timestamps, authors, submission information, and changes.

[0099] Collect defect tracking data related to the code and obtain performance logs of the application, including but not limited to response time, memory usage, CPU usage, etc. Collect the results of unit testing, integration testing, and system testing, including information such as whether the test passed and test coverage.

[0100] Collect project-related documentation, such as requirement documents, design documents, and user manuals.

[0101] Collect user feedback information, which may include user comments and suggestions on software performance and functions.

[0102] Furthermore, invalid, erroneous, and incomplete records in the dataset are removed. For example, correct erroneous code submission records and delete incomplete test results. Convert the collected data into a unified format for easy processing and analysis. For example, convert log files of different formats into structured log data. Extract useful features from the raw data, which can represent different aspects of the code, such as code complexity, code style, and performance indicators. For models that require supervised learning, the data needs to be labeled. For example, label code defects and performance bottlenecks. Convert the data into a format suitable for machine learning model input. For example, convert text data into vector representation and convert time series data into a format suitable for time series analysis. Normalize or standardize the data to eliminate the effects of different dimensions and magnitudes. Divide the dataset into training, validation, and test sets to facilitate model training and evaluation. When necessary, data augmentation techniques can be used to increase the diversity of the dataset and improve the generalization ability of the model. Build a knowledge base to store information such as code patterns, historical optimization cases, and best practices to provide reference for intelligent optimization.

[0103] Model training: Using preprocessed data, train machine learning models to identify patterns in the code, potential defects, performance bottlenecks, and areas that can be optimized.

[0104] Example 2 Figure 3 The following is a schematic diagram of the operation flow of the code optimization and reconstruction system provided by the embodiment of the present invention. The operation of the system includes the following steps: S31, receiving source code imported by the user through the visual interface.

[0105] S32, analyzes the syntax, structure, pattern, performance, and potential issues of source code through machine learning models.

[0106] S33 uses deep learning algorithms to understand the logic and functions in the code, and automatically identifies and optimizes redundancy in the code.

[0107] S34, generating optimization suggestions based on the analysis results of S32 and S34.

[0108] S35, displaying the optimization suggestions on a visual interface and receiving user feedback.

[0109] S36, optimizing the code based on user feedback, and displaying the optimized code.

[0110] Optionally, when displaying the optimized code, a comparison chart of the code before and after optimization can also be displayed so that developers can intuitively see the optimized parts.

[0111] The system also provides a code download function. When the user is satisfied with the results of code optimization, he can download the optimized code. If the user is not satisfied with the results of code optimization, he can continue to submit modification suggestions, and the system will optimize again based on the modification suggestions.

[0112] S37, monitor and evaluate the optimization process.

[0113] Monitoring: First, determine the monitoring objectives, including code quality indicators, performance indicators, and system stability. Design a monitoring strategy and clarify the scope, frequency, and methods of monitoring. Secondly, select appropriate monitoring tools, such as APM tools, log analysis systems, and performance analyzers, and integrate monitoring tools into development environments, test environments, and production environments to achieve real-time monitoring, set monitoring alarms, and promptly notify relevant personnel when indicators exceed preset thresholds.

[0114] Assessment: Use code quality assessment tools to regularly check code quality. Pay attention to quality indicators such as code complexity and potential defects. Generate monitoring and assessment reports regularly to summarize trends and potential problems of key indicators.

[0115] S38, Adaptive learning and improvement.

[0116] Specifically, various types of data generated during the operation of the system are collected, including user feedback data, code change data, optimization results, monitoring data, etc., to ensure that data collection complies with privacy and security standards.

[0117] The collected data is then analyzed to identify patterns, trends, and potential problems.

[0118] Use statistical analysis and machine learning to extract valuable information from data, use user feedback on optimization suggestions and reconstruction operations as important input, use the collected data to train and optimize machine learning models, adjust model parameters and learning algorithms based on performance evaluation results, and update the rule base and optimization strategies in the system based on analysis results, establish a closed-loop feedback mechanism to ensure that learning results can be fed back into the system. Conduct user satisfaction surveys regularly to collect users' overall evaluation of the system and suggestions for improvement. Adjust product roadmaps and development priorities based on user feedback. Regularly evaluate and adopt new technologies to maintain the advancement and competitiveness of the system.

[0119] The code optimization and refactoring system uses machine learning technology to analyze existing code bases and identify patterns, structures, and potential problems. Through deep learning algorithms, the system can understand the logic and functions of the code, automatically identify and optimize redundant and inefficient parts of the code, and then make optimization suggestions, including performance improvement, memory usage optimization, and code structure simplification. At the same time, it intelligently refactors the code structure according to software requirements and architectural changes to improve code quality and operating efficiency.

[0120] The core advantage of the code optimization and refactoring system lies in its adaptive ability, which can be adjusted according to the specific needs of the project and the working habits of the development team. It supports multiple programming languages ​​and frameworks and is highly scalable. In addition, the system provides a user-friendly interface that enables developers to easily view optimization suggestions, evaluate their impact, and decide whether to adopt them.

[0121] The code optimization and refactoring system not only reduces the time of manual code review, but also improves the maintainability and readability of the code. It helps the team achieve a more efficient and reliable software development process through continuous learning and adaptation.

[0122] Each module in the above code optimization and reconstruction system can be implemented in whole or in part by software, hardware and their combination. Each module can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to each module above.

[0123] An embodiment of the present invention also provides a computer program product, which includes a complete and highly modular instruction set, which not only covers the infrastructure and core algorithm implementation of the code optimization and reconstruction system, but also includes the latest security protocols and efficient data processing algorithms to ensure the efficiency and security of the system operation; it also provides a dynamic link library (DLL) and an application programming interface (API) to facilitate seamless connection with third-party development tools and continuous integration / continuous deployment (CI / CD) processes, thereby enhancing the flexibility and scalability of the system; it pre-installs configuration management tools and automated deployment scripts to simplify the system deployment process and ensure rapid online launch and version update capabilities.

[0124] It should be pointed out that the specific implementation methods described above can enable those skilled in the art to understand the invention more comprehensively, but do not limit the invention in any way. Therefore, although the invention has been described in detail in this specification and embodiments, those skilled in the art should understand that the invention can still be modified or replaced by equivalents; and all technical solutions and improvements that do not deviate from the spirit and scope of the invention are included in the protection scope of the patent for the invention. Any figure mark in the claims should not be regarded as limiting the claims involved. Any simple change or equivalent replacement of the technical solution that can be obviously obtained by any technician familiar with the field within the technical scope disclosed in the present invention belongs to the protection scope of the present invention.

Claims

1. A code optimization and reconstruction system, characterized in that: include: A code parsing module is used to analyze the structure, behavior and running performance of the initial source code of the program through a code parser, and perform preliminary repairs on the initial source code according to the analysis results to obtain the target source code; The requirements and architecture analysis module is used to analyze the consistency between the program requirements and the target source code architecture, and monitor the changes in program requirements. When inconsistencies between requirements and architecture are identified, or requirements change, the code architecture and mode are recommended based on the consistency analysis results and requirement changes. The optimization and refactoring module is used to analyze the code patterns, performance bottlenecks and potential defects of code changes in the target source code; based on the analysis results and the code architecture and patterns recommended by the requirements and architecture analysis module, it generates optimization and refactoring suggestions or automatically optimizes and refactors the code.

2. The code optimization and reconstruction system according to claim 1, characterized in that: Also includes: The testing and verification module is used to generate test cases and test the optimized and refactored code; A user interaction module is used to display optimization and reconstruction suggestions and optimization and reconstruction results, and receive user feedback data on the optimization and reconstruction suggestions and optimization and reconstruction results; The adaptive learning module is used to obtain user feedback data and code change data, and optimize the code optimization and refactoring strategies of the code optimization and refactoring system based on the user feedback data and code change data.

3. The code optimization and reconstruction system according to claim 1 or 2, characterized in that: The code parser includes a static analysis engine and a dynamic analysis engine. Through the static analysis engine, the initial source code is subjected to lexical analysis, syntax analysis, semantic analysis, data flow analysis, control flow analysis, symbolic execution, and defect detection. The dynamic analysis engine simulates the real operating environment of the program, analyzes the program's operating status, performance and security performance in the simulated environment, and obtains the analysis results.

4. The code optimization and reconstruction system according to claim 1 or 2, characterized in that: The optimization and reconstruction modules include: An unsupervised learning algorithm unit, used to identify code patterns of target source code through clustering technology; Time series analyzer, used to analyze the changing trend of the time series composed of performance data and determine the cause of performance bottlenecks; A supervised learning unit for predicting potential defects in code changes through pre-trained machine learning models.

5. The code optimization and reconstruction system according to claim 4, characterized in that: The optimization and reconstruction module also includes: The principal component analysis unit is used to perform dimensionality reduction processing on the high-dimensional feature space of the target source code and extract key features of the target source code.

6. The code optimization and reconstruction system according to claim 2, characterized in that: The testing and verification modules include: MBT generator, used to automatically generate test cases; A heuristic search algorithm optimizer is used to select some test cases from the test cases generated by the MBT generator through a genetic algorithm and optimize the parameter configuration of some test cases; Automatic testing framework integrator, used to execute optimized test cases and generate test reports.

7. The code optimization and reconstruction system according to claim 2 or 5, characterized in that: The adaptive learning module includes the agent, reward function and reinforcement learning model: The intelligent agent is used to perform code refactoring operations or code optimization operations based on preset strategies; The reward function is designed based on user feedback data and code change data, and is used to reward the execution results of the agent's code refactoring operations or code optimization operations, so that the agent updates the reinforcement learning model based on the reward to optimize the performance of the code optimization and refactoring system.

8. The code optimization and reconstruction system according to claim 2, characterized in that: The user interaction module uses interactive heat maps and three-dimensional models to display performance comparisons and impact assessments before and after code improvements. It also uses an intelligent feedback loop mechanism to integrate user feedback data into the adaptive learning module for learning and adaptation.

9. The code optimization and reconstruction system according to claim 1 or 2, characterized in that: The requirements and architecture analysis module analyzes the consistency between requirements and target source code architecture based on natural language processing technology and code parsing tools.

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