Auto-grading C programming assignments with CodeBERT and Random Forest Regressor

Grading coding assignments manually is challenging due to complexity and subjectivity. However, auto-grading with deep learning simplifies the task. It objectively assesses code quality, detects errors, and assigns marks accurately, reducing the burden on instructors while ensuring efficient and fair assessment. This study provides an analysis of auto-grading of the C programming assignments using machine learning and deep learning approaches like regression, convolutional neural networks (CNN) and long short-term memory (LSTM). Using a code-based transformer word embedding model called CodeBERT, the textual code inputs were transformed into vectors, and the vectors were then fed into several models. The testing findings demonstrated the efficacy of the suggested strategy with a root mean squared error (RMSE) of 1.89. The contrast between statistical methods and deep learning techniques is discussed in the study.