AI-based Online Code Quality Assessment System

Assessing students' code quality is a major workload for teachers, making research on automated systems crucial. We developed an AI-based system to automate code quality assessment, providing immediate feedback to students and reducing teachers' burden. We evaluated multiple machine learning models, including Multilayer Perceptron (MLP), k- Nearest Neighbors (k-NN), Naive Bayes (NB), Support Vector Machine (SVM), and Convolutional Neural Networks (CNN). Results show that the MLP model demonstrates the best performance, with an accuracy of 0.903, and our system can effectively identify code quality issues and improve the efficiency of educational feedback.