Automated AI Code Review Workflow for Student Assignments

Automated Code Review for Student Assignments

1. Assignment Submission

1.1 Student Uploads Code

Students submit their coding assignments through a designated online platform.

1.2 Confirmation of Submission

Upon submission, students receive an automated confirmation email with a summary of their assignment details.

2. Initial Code Analysis

2.1 Code Parsing

The system utilizes AI-driven tools such as SonarQube or DeepCode to parse the submitted code for initial syntax errors and formatting issues.

2.2 Preliminary Feedback Generation

AI algorithms analyze the code to generate preliminary feedback, highlighting potential errors and suggesting improvements.

3. Advanced Code Review

3.1 Code Quality Assessment

Tools like Codacy or CodeClimate assess the quality of the code based on predefined coding standards and best practices.

3.2 Security Vulnerability Detection

AI-driven security analysis tools such as Checkmarx or Snyk are employed to identify vulnerabilities in the code.

4. Feedback Compilation

4.1 Consolidation of Review Results

The system compiles feedback from various AI tools into a comprehensive report for the student.

4.2 Personalized Suggestions

Using natural language processing, the AI generates personalized suggestions for improvement based on the student’s coding habits and previous submissions.

5. Delivery of Feedback

5.1 Automated Report Generation

An automated report is generated, summarizing the findings of the code review, including strengths, weaknesses, and suggestions for improvement.

5.2 Notification to Student

The student receives an email notification that their code review is complete, along with a link to access their detailed feedback report.

6. Follow-Up and Revision

6.1 Student Review of Feedback

Students are encouraged to review the feedback and make necessary revisions to their assignments.

6.2 Resubmission Process

Students can resubmit their assignments for further review, allowing for iterative learning and improvement.

7. Continuous Improvement of AI Tools

7.1 Data Collection and Analysis

Data from student submissions and feedback is collected to enhance the performance of AI tools through machine learning.

7.2 Tool Optimization

Regular updates and optimizations to the AI tools are implemented based on user feedback and emerging coding standards.