Program Repair with Minimal Edits Using CodeT5
Atsushi Shirafuji Md. Mostafizer Rahman Md Faizul Ibne Amin Yutaka Watanobe
Programmers often struggle to identify and fix bugs in their programs. In
recent years, many language models (LMs) have been proposed to fix erroneous
programs and support error recovery. However, the LMs tend to generate
solutions that differ from the original input programs. This leads to potential
comprehension difficulties for users. In this paper, we propose an approach to
suggest a correct program with minimal repair edits using CodeT5. We fine-tune
a pre-trained CodeT5 on code pairs of wrong and correct programs and evaluate
its performance with several baseline models. The experimental results show
that the fine-tuned CodeT5 achieves a pass@100 of 91.95% and an average edit
distance of the most similar correct program of 6.84, which indicates that at
least one correct program can be suggested by generating 100 candidate
programs. We demonstrate the effectiveness of LMs in suggesting program repair
with minimal edits for solving introductory programming problems.