TY - GEN
T1 - Context-aware and data-driven feedback generation for programming assignments
AU - Song, Dowon
AU - Lee, Woosuk
AU - Oh, Hakjoo
N1 - Funding Information:
This work was supported by Institute of Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) (No.2020-0-01337,(SW STAR LAB) Research on Highly-Practical Automated Software Repair) and Sam-sung Research Funding & Incubation Center of Samsung Electronics under Project Number SRFC-IT1701-51. This research was partly supported by the MSIT(Ministry of Science and ICT), Korea, under the ICT Creative Consilience program(IITP-2021-2020-0-01819) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2020R1C1C1014518, 2021R1A5A1021944).
Publisher Copyright:
© 2021 ACM.
PY - 2021/8/20
Y1 - 2021/8/20
N2 - 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.
AB - 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.
KW - Program Repair
KW - Program Synthesis
UR - http://www.scopus.com/inward/record.url?scp=85116234038&partnerID=8YFLogxK
U2 - 10.1145/3468264.3468598
DO - 10.1145/3468264.3468598
M3 - Conference contribution
AN - SCOPUS:85116234038
T3 - ESEC/FSE 2021 - Proceedings of the 29th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering
SP - 328
EP - 340
BT - ESEC/FSE 2021 - Proceedings of the 29th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering
A2 - Spinellis, Diomidis
PB - Association for Computing Machinery, Inc
T2 - 29th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2021
Y2 - 23 August 2021 through 28 August 2021
ER -