UML Diagrams

Basic working of our proposed project and its UML diagrams

import base64
from IPython.display import Image, display
import matplotlib.pyplot as plt

def mm(graph):
    graphbytes = graph.encode("utf8")
    base64_bytes = base64.b64encode(graphbytes)
    base64_string = base64_bytes.decode("ascii")
    display(Image(url="https://mermaid.ink/img/" + base64_string))

Sequence Diagram

The sequence diagram that follows shows how the developer, IDE, model, and training process interact with one another. The process begins with the developer writing code in the IDE, the IDE sending the model the current code snippet, and the model using the machine learning model it has been trained to produce recommendations. After that, the developer can engage with the recommendations and offer input that helps the model get better over time by periodically retraining it. As the developer integrates the recommendations into their code, the process proceeds.

mm("""
sequenceDiagram
    participant Developer
    participant IDE
    participant Model
    participant Dataset
    participant MLTraining

    Developer->>IDE: Starts coding in IDE
    IDE->>Model: Developer's current code snippet
    Model->>MLTraining: Retrieve context from model
    MLTraining-->>Model: Trains machine learning model
    Model-->>IDE: Trained model

    alt Code Completion Activated
        Developer->>IDE: Continues typing
        IDE->>Model: Current code snippet
        Model->>Model: Generate code suggestions
        Model-->>IDE: List of suggestions
    else User Interaction
        Developer->>IDE: Reviews and selects suggestions
        IDE->>Model: User feedback
        Model-->>MLTraining: Update model with feedback
        MLTraining-->>Model: Retrain model
    end

    Developer-->>IDE: Continues coding with suggestions
""")

Class Diagram

The primary components and their interactions in an intelligent code completion system are depicted in this class diagram, which also emphasizes the information flow between the training component, the machine learning model, the integrated development environment, and the developer. However they might now reflect same in the code as we might change them according to the need.

mm("""
classDiagram
  class Developer {
    +writeCode()
  }

  class IDE {
    +sendCodeSnippet()
  }

  class Model {
    +generateCodeSuggestions()
    +receiveUserFeedback()
  }

  class MLTraining {
    +trainModel()
    +updateModel()
  }

  Developer --> IDE: Uses
  IDE --> Model: Sends code snippet
  Model --> MLTraining: Trains on dataset
  Model --> IDE: Sends suggestions
  Model --> MLTraining: Receives user feedback
  MLTraining --> Model: Updates model
""")

State Machine Diagram

The system is shown in the state machine diagram as “Coding” upon startup. It changes to the “Code Suggestions” state when code completion is enabled. In the event that the user interacts with the suggestions, the system can either move to the “User Interaction” stage or revert to the “Coding” state. The “User Interaction” state permits the developer to type further or approve/disapprove recommendations.

mm("""
stateDiagram
  state "Coding" as Coding
  state "Code Suggestions" as Suggestions
  state "User Interaction" as Interaction

  [*] --> Coding

  Coding --> Suggestions: Code Completion Activated
  Suggestions --> Coding: Suggestions Rejected
  Suggestions --> Interaction: User Interaction

  Interaction --> Suggestions: Continue Typing
  Interaction --> Coding: Suggestions Accepted
""")

User Journey Diagram

The steps a developer takes to use the intelligent code completion system and get started with coding in the IDE are shown in this user journey diagram. Code completion, suggestion creation, user interaction, and the loop of continual improvement via user feedback and model retraining are all included.

mm("""
journey
  title Developer's Journey with Intelligent Code Completion

  section Getting Started
    Developer --> IDE: Starts coding in IDE

  section Code Completion Activated
    IDE --> Model: Developer's current code snippet
    Model --> MLTraining: Retrieve context from 70GB C code dataset
    MLTraining -->> Model: Train machine learning model
    Model -->> IDE: Trained model
    Developer --> IDE: Continues typing

  section Suggestions
    IDE --> Model: Current code snippet
    Model --> Model: Generate code suggestions
    Model -->> IDE: List of suggestions
    Developer --> IDE: Reviews and selects suggestions

  section User Interaction
    IDE --> Model: User feedback
    Model -->> MLTraining: Update model with feedback
    MLTraining -->> Model: Retrain model
    Model -->> IDE: Updated model
    Developer --> IDE: Continues coding with suggestions

  section Conclusion
    Developer --> IDE: Finishes coding

""")