Large Language Models for Automated Program Repair

This paper introduces two methods for automated program repair (APR) utilizing pre-trained language models. The first method demonstrates program repair as a code completion task and is validated on a dataset of Java programs. The second method, Mentat, leverages OCaml's parser and type system as fault localization techniques to generate prompts for GPT-3, producing candidate patches. Evaluation results show promising repair rates, with 27% and 39.2% effectiveness, respectively. For OCaml, a comparative study employing an automated validation strategy is presented in which the technique outperforms other tools. Language models are effective at APR, enhancing bug fixing and freeing developers to focus on other critical aspects of software engineering.