Automated Program Repair Using Generative Models for Code Infilling

Charles Koutcheme, Sami Sarsa, Juho Leinonen, Arto Hellas, Paul Denny

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

4 Sitaatiot (Scopus)
2 Lataukset (Pure)

Abstrakti

In educational settings, automated program repair techniques serve as a feedback mechanism to guide students working on their programming assignments. Recent work has investigated using large language models (LLMs) for program repair. In this area, most of the attention has been focused on using proprietary systems accessible through APIs. However, the limited access and control over these systems remain a block to their adoption and usage in education. The present work studies the repairing capabilities of open large language models. In particular, we focus on a recent family of generative models, which, on top of standard left-to-right program synthesis, can also predict missing spans of code at any position in a program. We experiment with one of these models on four programming datasets and show that we can obtain good repair performance even without additional training.
AlkuperäiskieliEnglanti
OtsikkoArtificial Intelligence in Education : 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings
ToimittajatNing Wang, Genaro Rebolledo-Mendez, Noboru Matsuda, Olga C. Santos, Vania Dimitrova
KustantajaSpringer
Sivut798–803
ISBN (elektroninen)978-3-031-36272-9
ISBN (painettu)978-3-031-36271-2
DOI - pysyväislinkit
TilaJulkaistu - 2023
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Conference on Artificial Intelligence in Education - Tokyo, Japani
Kesto: 3 heinäk. 20237 heinäk. 2023
Konferenssinumero: 24

Julkaisusarja

NimiLecture Notes in Computer Science
KustantajaSpringer
Vuosikerta13916
ISSN (painettu)0302-9743

Conference

ConferenceInternational Conference on Artificial Intelligence in Education
LyhennettäAIED
Maa/AlueJapani
KaupunkiTokyo
Ajanjakso03/07/202307/07/2023

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