REFERENT: Transformer-based Feedback Generation using Assignment Information for Programming Course

Students require feedback on programming assignments to improve their programming skills. An Automated feedback generation (AFG) technique proposes to provide feedback-corrected submissions for incorrect student programming submissions in programming courses. However, these techniques are limited as they rely on the availability of correct submissions as a reference to generate feedback. In situations where correct submissions are not available, they resort to using mutation operators, which can lead to a search space explosion problem. In this work, we propose REFERENT, Transformer-based feedback generation using assignment information. REFERENT uses transfer learning on a pre-trained model with data from students’ submission history from the past assignment. To generate assignment-related feedback, we use a title, tag, assignment description, and test case as assignment information. REFERENT can generate feedback without a reference program in limited resources. We conducted a preliminary study to confirm the effectiveness of REFERENT and the feasibility of using assignment information. REFERENT generated feedback for 32.7% of incorrect submissions without reference programs and that its performance increased up to 50.7% when reference programs were used. We also check whether the submission history, assignment information, and repair knowledge of open-source software help generate feedback.