Java code generation and self-repairing system

Abstract

The invention discloses a Java code generation and self-repairing system, which comprises a natural language processing module, a data processing module, a self-repairing module and a self-repairing module, the code generation module is used for generating code snippets or complete codes according with Java grammar specifications and coding habits through a deep learning model; and the intelligent repair module is used for performing static analysis and dynamic test on the Java code to find errors and defects in the code. According to the method, advanced technologies such as deep learning and natural language processing are introduced, high-quality and maintainable codes are automatically generated according to natural language description or specific requirements of developers, and the flexibility and maintainability of code generation are improved.

Description

Technical Field

[0001] The invention belongs to the technical field of automatic code generation technology, and specifically is a Java code generation and self-repair system. Background Art

[0002] With the rapid development of the software industry, the efficiency and quality of code writing are crucial to the success of a project. However, traditional code writing and repairing methods often rely on the personal experience and skills of developers, and have problems such as low efficiency, high error rate, and difficulty in maintenance. Therefore, developing a system that can automatically generate and intelligently repair high-quality Java code is of great significance to improving software development efficiency and reducing maintenance costs.

[0003] The current pace of technological progress is relatively slow in some areas, making it difficult for developers to keep pace with the development of the industry, which in turn affects the innovation atmosphere and development prospects. Many unified standards and specifications related to technology have not yet been issued, resulting in differences in some technologies, which has had an adverse impact on industry activities and investment activities. The imperfect industry regulatory mechanism and insufficient supervision have led to internal chaos, technological differences, and price uncertainty in the industry, which have seriously restricted the development of the industry. Technology is greatly affected by the external environment. The constant changes in social and economic conditions and the interference of external factors have limited the development of technology, and some technologies have become outdated. Some technologies have security risks and may cause serious damage to users' information, privacy, and data, and also cause financial losses to users. The cross-border application of technology is difficult, and many technologies are not easy to apply across regions and industries. Therefore, it is more difficult to share and cooperate resources between industries, affecting the development of the industry. Summary of the invention

[0004] In view of the above problems, the present invention is proposed to provide a Java code generation and self-repair system that overcomes the above problems or at least partially solves the above problems.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A Java code generation and self-repair system, the system comprising:

[0007] Natural language processing module, used to extract programming requirements and intentions;

[0008] The code generation module generates code snippets or complete codes that conform to Java syntax specifications and coding habits through deep learning models;

[0009] The intelligent repair module is used to perform static analysis and dynamic testing on Java code to find errors and defects in the code.

[0010] Optionally, the natural language processing module includes:

[0011] The model selection unit processes the developer’s natural language description based on the pre-trained model;

[0012] The domain knowledge base unit builds a professional vocabulary base and programming habit base in the Java programming field to assist the natural language processing module in parsing and semantic expansion;

[0013] The semantic analysis unit encodes the natural language description through a pre-trained model and captures the dependencies in the text through a self-attention mechanism. It also combines the domain knowledge base to semantically expand and normalize the encoded representation to generate structured programming requirements.

[0014] Optionally, the code generation module includes:

[0015] A data preprocessing unit, used to collect Java code data and perform preprocessing;

[0016] The model building unit uses a deep learning model based on Seq2Seq. The encoder converts structured programming requirements into vector representations, and the decoder generates corresponding Java code based on the vector representations.

[0017] Model training and optimization unit, which updates model parameters through back-propagation algorithm and gradient descent optimizer, and introduces attention mechanism and replication mechanism;

[0018] The code quality assessment unit uses code quality assessment tools to perform style checks, complexity analysis, and security vulnerability detection on the generated code, and modifies and optimizes the generated code based on the assessment results.

[0019] Optionally, the preprocessing includes removing comments, formatting codes, and extracting functions and variables.

[0020] Optionally, the deep learning model is an encoder-decoder model of a Transformer architecture.

[0021] Optionally, the intelligent repair module includes:

[0022] Static code analysis unit, which uses static code analysis tools to perform syntax analysis, control flow analysis, and data flow analysis on Java code to identify errors and defects in the code;

[0023] Error classification and location unit, which uses deep learning models to automatically classify and locate the results of static code analysis;

[0024] The repair suggestion generation unit generates multiple repair suggestions based on the error type and code context;

[0025] The repair effect verification unit verifies the effectiveness of repair suggestions through simulation tests and integration tests.

[0026] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0027] 1. The present invention introduces advanced technologies such as deep learning and natural language processing to automatically generate high-quality, maintainable code based on the developer's natural language description or specific needs, thereby improving the flexibility and maintainability of code generation.

[0028] 2. The present invention reduces dependence on data and computing resources, improves resource utilization efficiency, and reduces system costs by optimizing algorithms and model design.

[0029] 3. The present invention facilitates the integration and upgrading of subsequent technologies by designing a flexible and scalable system architecture and modules to meet the ever-changing needs and technological development. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A schematic diagram of a Java code generation and self-repair system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0032] See also Figure 1 , this embodiment provides a Java code generation and self-repair system, the system comprising:

[0033] The natural language processing module uses natural language processing technology to parse and understand the natural language descriptions of developers, extract programming requirements and intentions, and combine domain knowledge base and semantic analysis technology to semantically expand and normalize the parsing results to form structured programming requirements.

[0034] The natural language processing module includes:

[0035] The model selection unit processes the developer's natural language description based on the pre-trained model; these models have good semantic understanding and text generation capabilities through training on massive text data.

[0036] The domain knowledge base unit builds a professional vocabulary base and programming habit base in the Java programming field to assist the natural language processing module in parsing and semantic expansion. The vocabulary base contains Java keywords, class names, method names, etc., while the programming habit base contains common programming patterns and best practices.

[0037] The semantic analysis unit encodes the natural language description through a pre-trained model and captures the dependencies in the text through a self-attention mechanism. It also combines the domain knowledge base to semantically expand and normalize the encoded representation to generate structured programming requirements.

[0038] The code generation module generates code snippets or complete codes that conform to Java syntax specifications and coding habits through deep learning models based on structured programming requirements. It quickly generates high-quality codes and reduces repetitive work by means of template matching and code snippet combination. It introduces a code quality assessment mechanism to analyze and evaluate the quality of the generated code to ensure the readability, maintainability and security of the code.

[0039] The code generation module includes:

[0040] The data preprocessing unit is used to collect Java code data and perform preprocessing; the preprocessing includes removing comments, formatting code, extracting functions and variables. A code library containing rich programming elements and patterns is built to train the code generation model.

[0041] The model building unit adopts a deep learning model based on Seq2Seq. The encoder converts structured programming requirements into vector representations, and the decoder generates corresponding Java code based on the vector representations. The deep learning model is an encoder-decoder model of the Transformer architecture.

[0042] The model training and optimization unit uses a large amount of Java code data for model training, updates model parameters through the back-propagation algorithm and gradient descent optimizer, and introduces attention and replication mechanisms to improve the model's attention to key programming elements and patterns and its generation accuracy.

[0043] The code quality assessment unit uses code quality assessment tools such as Checkstyle and PMD to perform style checks, complexity analysis, and security vulnerability detection on the generated code, and modifies and optimizes the generated code based on the assessment results to ensure the quality and maintainability of the code.

[0044] The intelligent repair module is used to perform static analysis and dynamic testing on Java code to find errors and defects in the code. It uses deep learning models to automatically classify and locate errors and defects, reducing the time cost of manual search and location. It provides intelligent repair suggestions, including code modification, refactoring, optimization and other solutions, to help developers quickly fix problems. It verifies the repair effect through simulation testing and integration testing to ensure the correctness and reliability of the repair.

[0045] The intelligent repair module includes:

[0046] The static code analysis unit uses static code analysis tools such as SonarQube and FindBugs to perform syntax analysis, control flow analysis, and data flow analysis on Java code to identify errors and defects in the code, such as null pointer exceptions and resource leaks.

[0047] The error classification and location unit uses a deep learning model to automatically classify and locate the results of static code analysis; by learning from a large number of labeled error cases, the model can accurately identify the error type and locate the specific line of code.

[0048] The repair suggestion generation unit generates a variety of repair suggestions based on the error type and code context; the repair suggestions may include code modification, refactoring, optimization and other solutions, and provide corresponding explanations and sample codes.

[0049] The repair effect verification unit verifies the effectiveness of repair suggestions through simulation tests and integration tests, and provides a visual repair interface and interactive repair guidance to help developers better understand and apply repair suggestions.

[0050] This embodiment introduces advanced technologies such as deep learning and natural language processing to automatically generate high-quality, maintainable code based on the developer's natural language description or specific needs, thereby improving the flexibility and maintainability of code generation.

[0051] This embodiment reduces dependence on data and computing resources, improves resource utilization efficiency, and reduces system costs by optimizing algorithms and model design.

[0052] This embodiment designs a flexible and scalable system architecture and modules to facilitate the integration and upgrading of subsequent technologies to meet ever-changing needs and technological developments.

[0053] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the scope of protection of the present invention.

Claims

1. A Java code generation and self-repair system, It is characterized in that The system comprises: Natural language processing module, used to extract programming requirements and intentions; The code generation module generates code snippets or complete codes that conform to Java syntax specifications and coding habits through deep learning models; The intelligent repair module is used to perform static analysis and dynamic testing on Java code to find errors and defects in the code.

2. A Java code generation and self-repair system as claimed in claim 1, It is characterized in that The natural language processing module includes: The model selection unit processes the developer’s natural language description based on the pre-trained model; The domain knowledge base unit builds a professional vocabulary base and programming habit base in the Java programming field to assist the natural language processing module in parsing and semantic expansion; The semantic analysis unit encodes the natural language description through a pre-trained model and captures the dependencies in the text through a self-attention mechanism. It also combines the domain knowledge base to semantically expand and normalize the encoded representation to generate structured programming requirements.

3. A Java code generation and self-repairing system as claimed in claim 1, It is characterized in that The code generation module includes: A data preprocessing unit, used to collect Java code data and perform preprocessing; The model building unit uses a deep learning model based on Seq2Seq. The encoder converts structured programming requirements into vector representations, and the decoder generates corresponding Java code based on the vector representations. Model training and optimization unit, which updates model parameters through back-propagation algorithm and gradient descent optimizer, and introduces attention mechanism and replication mechanism; The code quality assessment unit uses code quality assessment tools to perform style checks, complexity analysis, and security vulnerability detection on the generated code, and modifies and optimizes the generated code based on the assessment results.

4. A Java code generation and self-repairing system as claimed in claim 3, It is characterized in that The preprocessing includes removing comments, formatting codes, and extracting functions and variables.

5. A Java code generation and self-repairing system as claimed in claim 3, It is characterized in that The deep learning model is an encoder-decoder model of the Transformer architecture.

6. A Java code generation and self-repairing system as claimed in claim 1, It is characterized in that The intelligent repair module comprises: Static code analysis unit, which uses static code analysis tools to perform syntax analysis, control flow analysis, and data flow analysis on Java code to identify errors and defects in the code; Error classification and location unit, which uses deep learning models to automatically classify and locate the results of static code analysis; The repair suggestion generation unit generates multiple repair suggestions based on the error type and code context; The repair effect verification unit verifies the effectiveness of repair suggestions through simulation tests and integration tests.

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