@inproceedings{c2f67c2d334d460b8299dace66492bd6,
title = "RepairNet: Contextual Sequence-to-Sequence Network for Automated Program Repair",
abstract = "Compile-time errors can wreak havoc for programmers {\textendash} seasoned and novice. Often developers spend a lot of time debugging them. An automated system to repair such errors can be a useful aid to the developers for their productivity. In this work, we propose a deep generative model, RepairNet, that automatically repairs programs that fail at compile time. RepairNet is based on sequence-to-sequence modeling and uses both code and error messages to repair the program. We evaluated the effectiveness of our system on 6,971 erroneous submissions for 93 programming tasks. RepairNet outperforms the existing state-of-the-art technique, MACER, with 17\% relative improvement of repair accuracy. Our approach can fix 66.4\% of the erroneous submissions completely and 14.2\% partially.",
keywords = "Bug fixing, Program repair, Sequence modeling",
author = "Kumar Abhinav and Vijaya Sharvani and Alpana Dubey and Meenakshi D{\textquoteright}Souza and Nitish Bhardwaj and Sakshi Jain and Veenu Arora",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 22nd International Conference on Artificial Intelligence in Education, AIED 2021 ; Conference date: 14-06-2021 Through 18-06-2021",
year = "2021",
doi = "10.1007/978-3-030-78292-4\_1",
language = "English",
isbn = "9783030782917",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "3--15",
editor = "Ido Roll and Danielle McNamara and Sergey Sosnovsky and Rose Luckin and Vania Dimitrova",
booktitle = "Artificial Intelligence in Education - 22nd International Conference, AIED 2021, Proceedings",
}