Code recommendation system based on deep learning

Abstract

The invention relates to the technical field of deep learning, and discloses a code recommendation system based on deep learning, and the process of the code recommendation system based on deep learning comprises the following steps: data collection and preprocessing; training and optimizing the model; integrating and deploying the system; code recommendation and user interaction; and performing real-time feedback and model updating. According to the code recommendation system based on deep learning and enhanced context understanding, the advanced deep learning technology is adopted in the system, especially the context sensing neural network model is combined, context information of codes can be captured more accurately, and therefore code recommendation highly matched with the requirements of developers is provided; and through large-scale code library training and continuous optimization, the system can generate code snippets with correct grammar and rigorous logic. In addition, the system further integrates a code verification mechanism, and correctness and reliability of recommended codes in practical application are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of deep learning, and particularly to a code recommendation system based on deep learning. Background Art

[0002] The core of a code recommendation system based on deep learning lies in using deep learning technology to understand and predict code snippets that developers may need, thereby improving programming efficiency and accuracy. The code recommendation system based on deep learning has enhanced context understanding, adopting a deep learning model, especially the Transformer architecture combined with an attention mechanism, to more accurately capture the context information of the code. Improve code recommendation accuracy. Through training with large-scale datasets and a real-time feedback mechanism, ensure that the recommended code is not only syntactically correct but also logically consistent with the project. Have strong privacy and security protection measures. Design a localized data processing and encrypted communication mechanism to ensure the security and privacy of user code: Provide an offline working mode so that even without a network connection, developers can use basic code recommendation functions. Implement localized model training, allowing developers to train their own code recommendation models locally, thus avoiding uploading code data to the cloud.

[0003] Most code recommendation systems on the market currently require online registration and use, which is a major limitation for offline users or developers in a network-free environment. Without a network, these systems often cannot provide recommendation services. This limits the work efficiency of developers in remote locations, on airplanes, or in environments with unstable networks. There are limitations in understanding the context of the code, resulting in the recommended code snippets not matching or being inappropriate for the actual needs. Developers may need to spend extra time modifying or ignoring inaccurate recommendations, which instead reduces programming efficiency. Online code recommendation systems may require uploading code to the cloud, which is a potential security risk for code development involving sensitive information or trade secrets. Developers may avoid using these systems due to privacy and security concerns, thus missing the opportunity to improve efficiency. The systems on the market may not be able to make sufficient personalized adjustments for specific project requirements or individual coding styles. The recommended code may not conform to the conventions or best practices of a specific project, requiring developers to make a large number of modifications.

[0004] Currently, code completion recommendation systems on the market have a wide range of applications in the software development industry, greatly improving the work efficiency of developers. However, it also has some drawbacks:

[0005] 1. Insufficient context understanding. For example, some systems may not fully understand the context of the code, resulting in code snippets recommended not matching the actual requirements. These problems often stem from the model's insufficient understanding of code structure, programming intent, and project-specific logic.

[0006] 2. Recommendation accuracy issues. The recommended code may have syntax errors or logical mismatches, affecting development efficiency. This inaccuracy may be caused by insufficient model training or limitations of the dataset.

[0007] 3. Privacy and security issues. The code recommendation system may need to access sensitive code repositories, raising privacy and security concerns. Developers are worried about the risk of code leakage or improper use, which limits the widespread application of the code recommendation system.

[0008] Therefore, there is an urgent need for a code recommendation system based on deep learning to solve the above technical problems. Summary of the Invention

[0009] The purpose of the present invention is to provide a code recommendation system based on deep learning to solve the problems raised in the above background technology.

[0010] To achieve the above purpose, the present invention provides the following technical solutions:

[0011] A code recommendation system based on deep learning, the process of which includes the following:

[0012] Step 1: Data collection and preprocessing;

[0013] Step 2: Model training and optimization;

[0014] Step 3: System integration and deployment;

[0015] Step 4: Code recommendation and user interaction;

[0016] Step 5: Real-time feedback and model update.

[0017] Preferably, in Step 1, data collection collects Python code repositories from open-source code platforms such as GitHub and Gitee, downloads the corresponding code repositories to the local computer according to user needs and the user's own development preferences, or uses existing available offline code repositories.

[0018] Preferably, the data preprocessing in Step 1 includes the following steps:

[0019] S1. Cleaning: Remove invalid and incomplete code snippets;

[0020] S2. Tokenization: Convert the code into a token sequence and use the codebert-base library to tokenize the code;

[0021] S3. Serialization: Determine the maximum length of the sequence and create input-output pairs. Given a code sequence of length N, use the first N - 1 tokens as input and the last token as output;

[0022] S4. Encoding: Convert the tokens to integer indices and use these indices as input to the model.

[0023] Preferably, for model training and optimization in step two, use a computer with GPU acceleration or a GPU cluster, and use the TensorFlow or PyTorch framework for model training. During the training process, the following steps are adopted:

[0024] S1. Iterative optimization: Use the training set to perform multiple iterative trainings on the model until convergence;

[0025] S2. Hyperparameter tuning: Adjust hyperparameters such as the learning rate and batch size through the validation set to obtain the best model performance;

[0026] S3. Performance evaluation: Use the test set to evaluate the model to ensure that the model has good generalization ability.

[0027] Preferably, system integration and deployment include the following steps:

[0028] S1. IDE plugin development: Write IDE plugin code to achieve integration with mainstream IDEs such as Visual Studio Code and PyCharm;

[0029] S2. API interface implementation: Design and implement a RESTful API interface to allow the IDE plugin to communicate with the backend service in an encrypted manner;

[0030] S3. Backend service deployment: Deploy the model inference service and database on the server to ensure the stability and security of the service.

[0031] Preferably, code recommendation and user interaction include the following steps:

[0032] S1. Implement the following functions in the IDE plugin;

[0033] S2. Context capture: Monitor the user's coding activities in real time and capture the context of the current code editor;

[0034] S3. Recommendation generation: Send the captured context to the backend service through the API, and the backend service uses the trained model to generate recommended code snippets;

[0035] S4. Recommended display: Real-time display of recommended code in the IDE, and users can choose to accept or ignore the recommended code snippets.

[0036] Preferably, the real-time feedback and model update implement the following process in the backend service:

[0037] S1. Feedback collection: Record the usage of the recommended code by users, including the acceptance rate and modification situation.

[0038] S2. Model fine-tuning: Regularly analyze the collected feedback data and fine-tune the model to optimize the accuracy and relevance of code recommendations.

[0039] Preferably, the code recommendation system based on deep learning adopts a deep learning architecture based on Transformer, which is optimized for the context awareness ability of code to provide highly relevant code recommendations.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1. Context-aware deep learning model: Adopt a deep learning architecture based on Transformer, which is specifically optimized for the context awareness ability of code to provide highly relevant code recommendations.

[0042] 2. Localized data processing and privacy protection: All code analysis and recommendation generation processes are carried out in the user's local environment, combined with encryption communication technology to ensure the privacy and security of the user's code.

[0043] 3. Combination of offline working mode and cloud service: Provide an offline working mode to ensure that developers can also use the recommendation system in a network-free environment, and at the same time support the cloud service mode to provide more powerful computing capabilities.

[0044] 4. Real-time feedback learning mechanism: Implement a real-time feedback system that allows developers to evaluate the recommendation results, and the system dynamically adjusts the recommendation strategy accordingly to continuously optimize the model.

[0045] 5. Use a deep learning model to deeply understand the context of code, adopt multi-modal feature fusion technology to improve the accuracy of code recommendations, implement a real-time feedback learning mechanism, continuously optimize the recommendation results, ensure that data processing is carried out locally, and combine encryption technology to protect user privacy. Description of the Drawings

[0046] By reading the detailed description of the non-restrictive embodiments with reference to the following drawings, other features, purposes and advantages of the present application will become more obvious:

[0047] Figure 1Schematic diagram of the process of a code recommendation system based on deep learning according to the present invention. Detailed implementation manners

[0048] The present application will be further described in detail below with reference to the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related invention, rather than limiting the invention. Additionally, it should be noted that for the sake of convenience of description, only the parts related to the invention are shown in the accompanying drawings. In the drawings of the embodiments of the present invention: Different types of hatching lines in the figures are not marked according to the national standard, nor are the materials of the components required. It is to distinguish the cross-sectional views of the components in the figures.

[0049] Please refer to Figure 1 , a code recommendation system based on deep learning. The process of this code recommendation system based on deep learning includes the following:

[0050] Step 1: Data collection and preprocessing;

[0051] Step 2: Model training and optimization;

[0052] Step 3: System integration and deployment;

[0053] Step 4: Code recommendation and user interaction;

[0054] Step 5: Real-time feedback and model update.

[0055] Among them, in Step 1, data collection collects Python code repositories from open source code platforms such as GitHub and Gitee, downloads the corresponding code repositories to the local computer according to the user's needs and the user's own development preferences, or uses existing available offline code repositories.

[0056] Among them, the data preprocessing in Step 1 includes the following steps:

[0057] S1. Cleaning: Remove invalid and incomplete code snippets;

[0058] S2. Tokenization: Convert the code into a token sequence, and use the codebert-base library to tokenize the code;

[0059] S3. Serialization: Determine the maximum length of the sequence and create input-output pairs. Given a code sequence of length N, use the first N - 1 tokens as the input and the last token as the output;

[0060] S4. Encoding: Convert the tokens into integer indices and use these indices as the input to the model.

[0061] Among them, in step two, model training and optimization are carried out on a computer with GPU acceleration or a GPU cluster, using the TensorFlow or PyTorch framework for model training. During the training process, the following steps are adopted:

[0062] S1. Iterative optimization: Use the training set to perform multiple iterative trainings on the model until convergence;

[0063] S2. Hyperparameter adjustment: Adjust hyperparameters such as the learning rate and batch size through the validation set to obtain the best model performance;

[0064] S3. Performance evaluation: Use the test set to evaluate the model to ensure that the model has good generalization ability.

[0065] Among them, system integration and deployment include the following steps:

[0066] S1. IDE plugin development: Write IDE plugin code to achieve integration with mainstream IDEs such as Visual Studio Code and PyCharm;

[0067] S2. API interface implementation: Design and implement a RESTful API interface to allow the IDE plugin to communicate with the backend service in an encrypted manner;

[0068] S3. Backend service deployment: Deploy the model inference service and database on the server to ensure the stability and security of the service.

[0069] Among them, code recommendation and user interaction include the following steps:

[0070] S1. Implement the following functions in the IDE plugin;

[0071] S2. Context capture: Monitor the user's coding activities in real time and capture the context of the current code editor;

[0072] S3. Recommendation generation: Send the captured context to the backend service through the API, and the backend service uses the trained model to generate recommended code snippets;

[0073] S4. Recommendation display: Display the recommended code in the IDE in real time, and the user can choose to accept or ignore the recommended code snippets.

[0074] Among them, real-time feedback and model update implement the following process in the backend service:

[0075] S1. Feedback collection: Record the usage situation of the recommended code by the user, including the acceptance rate and modification situation;

[0076] S2. Model fine-tuning: Regularly analyze the collected feedback data and fine-tune the model to optimize the accuracy and relevance of code recommendation.

[0077] Among them, the deep learning-based code recommendation system adopts a deep learning architecture based on Transformer, optimizes the context awareness ability for code, and provides highly relevant code recommendations.

[0078] It should be noted that the technical application industries and scenarios of the deep learning-based code recommendation system focus on the software development industry, aiming to improve software development efficiency, reduce coding errors, and at the same time ensure the privacy and security of user code.

[0079] The following takes the Python language as an example for the deep learning-based code recommendation system to illustrate the implementation process of the present invention:

[0080] The flowchart is as Figure 1 shown:

[0081] 1. Data collection and preprocessing

[0082] Data collection

[0083] Collect Python code repositories from open source code platforms such as GitHub and Gitee, download the corresponding code repositories to the local computer according to user needs and the user's own development preferences, or use existing available offline code repositories.

[0084] Data preprocessing

[0085] Cleaning: Remove invalid and incomplete code snippets.

[0086] Tokenization: Convert the code into a sequence of tokens. Use the codebert-base library to tokenize the code.

[0087] Serialization: Determine the maximum length of the sequence and create input-output pairs. For example, given a code sequence of length N, the first N - 1 tokens can be used as input, and the last token as output.

[0088] Encoding: Convert the tokens to integer indices and use these indices as the input to the model.

[0089] 2. Model training and optimization

[0090] On a computer with GPU acceleration or a GPU cluster, use the TensorFlow or PyTorch framework for model training. During the training process, the following steps are adopted:

[0091] Iterative optimization: Use the training set to perform multiple iterative trainings on the model until convergence.

[0092] Hyperparameter Tuning: Adjust hyperparameters such as learning rate and batch size through the validation set to obtain the best model performance.

[0093] Performance Evaluation: Evaluate the model using the test set to ensure that the model has good generalization ability

[0094] 3. System Integration and Deployment

[0095] IDE Plugin Development: Write IDE plugin code to achieve integration with mainstream IDEs such as Visual Studio Code and PyCharm.

[0096] API Interface Implementation: Design and implement RESTful API interfaces to allow IDE plugins to communicate securely with backend services.

[0097] Backend Service Deployment: Deploy the model inference service and database on the server to ensure the stability and security of the service

[0098] 4. Code Recommendation and User Interaction

[0099] Implement the following functions in the IDE plugin:

[0100] Context Capture: Monitor the user's coding activities in real time and capture the context of the current code editor.

[0101] Recommendation Generation: Send the captured context to the backend service through the API, and the backend service uses the trained model to generate recommended code snippets.

[0102] Recommendation Display: Display the recommended code in the IDE in real time, and the user can choose to accept or ignore the recommended code snippets.

[0103] 5. Real-time Feedback and Model Update

[0104] Implement the following process in the backend service:

[0105] Feedback Collection: Record the usage of the recommended code by the user, including the acceptance rate, modification situation, etc.

[0106] Model Fine-tuning: Regularly analyze the collected feedback data and fine-tune the model to optimize the accuracy and relevance of code recommendations;

[0107] The emergence of the "Deep Learning-based Code Recommendation System" perfectly solves the above problems:

[0108] 1. Enhanced Context Understanding: This system adopts advanced deep learning technologies, especially a neural network model combined with context awareness, which can more accurately capture the context information of the code, thus providing code recommendations that highly match the developer's needs.

[0109] 2. Improve recommendation accuracy: Through large-scale code library training and continuous optimization, this system can generate code snippets with correct grammar and rigorous logic. In addition, the system also integrates a code verification mechanism to ensure the correctness and reliability of the recommended code in actual applications.

[0110] 3. Ensure privacy and security: Privacy and security have been fully considered in the design of this system. Localized processing and encrypted communication technologies are adopted to ensure the security of user code. At the same time, the system follows the principle of least privilege, only accessing the necessary code information to reduce the potential risk of privacy leakage.

[0111] The content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0112] The above description is only a preferred embodiment of this application and an explanation of the technical principles applied. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the technical solutions formed by mutually replacing the above features with the (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A code recommendation system based on deep learning, characterized by: The process of the deep learning-based code recommendation system includes the following: Step 1: Data collection and preprocessing; Step 2: Model training and optimization; Step 3: System integration and deployment; Step 4: Code recommendation and user interaction; Step 5: Real-time feedback and model update.

2. A code recommendation system based on deep learning according to claim 1, characterized in that: In step 1, data collection collects Python code repositories from open source code platforms such as GitHub and Gitee. According to user needs and the user's own development preferences, the corresponding code repository is downloaded to the local computer, or an existing available offline code repository is used.

3. The code recommendation system based on deep learning according to claim 1, characterized in that: The data preprocessing in step 1 includes the following steps: S1, cleaning: remove invalid and incomplete code snippets; S2. Word segmentation: Convert the code into a token sequence and use the codebert-base library to segment the code; S3, Serialization: Determine the maximum length of the sequence and create input-output pairs. Given a code sequence of length N, take the first N-1 tokens as input and the last token as output. S4. Encoding: Convert tokens to integer indices and use these indices as input to the model.

4. The code recommendation system based on deep learning according to claim 1, characterized in that: In step 2, model training and optimization are performed on a computer or GPU cluster with GPU acceleration using the TensorFlow or PyTorch framework. During the training process, the following steps are taken: S1, iterative optimization: use the training set to iterate the model multiple times until convergence; S2. Hyperparameter adjustment: adjust hyperparameters such as learning rate and batch size through the validation set to obtain the best model performance; S3. Performance evaluation: Use the test set to evaluate the model to ensure that the model has good generalization ability.

5. The code recommendation system based on deep learning according to claim 1, characterized in that: System integration and deployment includes the following steps: S1. IDE plug-in development, write IDE plug-in code, and integrate with mainstream IDEs such as Visual Studio Code and PyCharm; S2. API interface implementation: design and implement RESTful API interface to allow IDE plug-ins to communicate with backend services in encrypted form; S3, backend service deployment, deploy model inference services and databases on the server to ensure the stability and security of the service.

6. The code recommendation system based on deep learning according to claim 1, characterized in that: Code recommendation and user interaction include the following steps: S1. Implement the following functions in the IDE plug-in; S2, context capture: monitor the user's coding activities in real time and capture the context of the current code editor; S3, recommendation generation: The captured context is sent to the backend service through the API, and the backend service uses the trained model to generate recommended code snippets; S4. Recommended display: The recommended code is displayed in real time in the IDE, and users can choose to accept the recommended code snippet or ignore it.

7. The code recommendation system based on deep learning according to claim 1, characterized in that: Real-time feedback and model updates implement the following process in the backend service: S1. Feedback collection: record the user's use of the recommended code, including acceptance rate and modification status; S2. Model fine-tuning: Regularly analyze the collected feedback data and fine-tune the model to optimize the accuracy and relevance of code recommendations.

8. The code recommendation system based on deep learning according to claim 1, characterized in that: This deep learning-based code recommendation system adopts a Transformer-based deep learning architecture, optimizes the context-awareness of code, and provides highly relevant code recommendations.

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