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Integration tests

Overview​

Integration testing ensures that different components of the system work together as expected. These tests are designed to validate the interactions between the user, AI model, and system logic by simulating real-world scenarios using mock objects. External input is provided via mock objects, and results are verified programmatically, eliminating the need for manual data entry.

Testing Approach​

  • Each test case corresponds to a use case described in the system documentation.
  • Mock objects are used to simulate user actions and AI model responses.
  • Automated assertions validate expected system behavior.
  • Tests are designed to run without manual intervention and produce clear pass/fail results.

Integration Test Cases​

1. Context-Aware Code Suggestions​

Objective: Ensure that inline code suggestions are generated correctly and logged properly.

Test Steps:​

  • Simulate a user typing code in the editor.
  • Mock the AI model’s response with a correct and incorrect suggestion.
  • Verify that the system displays the suggestion inline.
  • Simulate user accepting/rejecting the suggestion.
  • Validate that the system logs the user’s choice, decision time, and correctness.

Expected Result:​

The suggestion is displayed correctly. The user’s decision and response time are logged. If the user accepts an incorrect suggestion, feedback is generated.

2. Inline Questioning about Code​

Objective: Ensure users can ask and receive responses for inline questions.

Test Steps:​

  • Simulate a user highlighting a piece of code and asking a question.
  • Mock the AI model’s response.
  • Verify that the system displays the AI-generated answer.
  • Simulate user requesting further clarification.
  • Validate that the system logs the question and user interactions.

Expected Result:​

The AI-generated response is displayed. User requests for clarification are handled. The system logs user engagement.

3. Decision Time Logging for Code Suggestions​

Objective: Ensure the system correctly tracks how long a user takes to accept or reject a suggestion.

Test Steps:​

  • Simulate the AI model providing a suggestion.
  • Start a timer when the suggestion is displayed.
  • Simulate user accepting/rejecting the suggestion.
  • Verify that the system logs the response time.
  • Mock repeated quick and incorrect selections.
  • Validate that the system flags the user for disengagement.

Expected Result:​

The system logs decision time accurately. Users who consistently make rapid incorrect selections are flagged.

4. Feedback After Selecting a Suggestion​

Objective: Ensure users receive feedback on their choice after accepting/rejecting a suggestion.

Test Steps:​

  • Simulate a user selecting a suggestion.
  • Mock the system verifying correctness.
  • Verify that the system provides confirmation for correct choices.
  • Verify that the system provides feedback for incorrect choices.
  • Validate that the system logs the mistake.

Expected Result:​

The system correctly provides feedback based on user choice. Mistakes are logged for future analysis.

5. Tracking and Logging User Decisions​

Objective: Ensure all user decisions regarding code suggestions are recorded.

Test Steps:​

  • Simulate multiple user interactions with code suggestions.
  • Verify that all accept/reject decisions are logged.
  • Validate correctness tracking.
  • Mock repeated incorrect choices.
  • Verify that recurring mistakes are flagged.

Expected Result:​

The system accurately tracks user decisions and logs errors. Recurring mistakes are flagged for instructor review.

6. Generating Learning Reports for Administrators​

Objective: Ensure the system compiles user data into meaningful reports.

Test Steps:​

  • Simulate multiple users interacting with the system.
  • Mock the AI model compiling performance reports.
  • Verify that reports include percentages of correct/incorrect responses.
  • Validate that the report flags topics students struggle with.

Expected Result:​

The learning report is correctly generated. Struggle areas are highlighted for instructors.

7. Monitoring Student Progress​

Objective: Ensure administrators can track students' learning over time.

Test Steps:​

  • Mock the AI model generating weekly progress reports.
  • Verify that student progress is categorized into improvement, plateau, or decline.
  • Validate that recurring student mistakes are flagged.

Expected Result:​

The system correctly identifies student progress trends. Instructors can use reports to improve teaching strategies.

8. AI-Generated Quizzes Based on Past Topics​

Objective: Ensure the AI model generates quizzes based on student activity.

Test Steps:​

  • Mock the AI model tracking weekly learning topics.
  • Simulate quiz generation based on past struggles.
  • Verify that users receive a personalized quiz.
  • Validate that the AI model logs correct and incorrect quiz responses.
  • Verify that users and administrators can review quiz results.

Expected Result:​

The AI model generates personalized quizzes. Users receive feedback on quiz performance. Administrators can analyze student weaknesses.