Context-aware and data-driven feedback generation for programming assignments

Recently, various techniques have been proposed to automatically provide personalized feedback on programming exercises. The cutting edge of which is the data-driven approaches that leverage a corpus of existing correct programs and repair incorrect submissions by using similar reference programs in the corpus. However, current data-driven techniques work under the strong assumption that the corpus contains a solution program that is close enough to the incorrect submission. In this paper, we present Cafe, a new data-driven approach for feedback generation that overcomes this limitation. Unlike existing approaches, Cafe uses a novel context-aware repair algorithm that can generate feedback even if the incorrect program differs significantly from the reference solutions. We implemented Cafe for OCaml and evaluated it with 4,211 real student programs. The results show that Cafe is able to repair 83 % of incorrect submissions, far outperforming existing approaches.