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2021 | OriginalPaper | Buchkapitel

RepairNet: Contextual Sequence-to-Sequence Network for Automated Program Repair

verfasst von : Kumar Abhinav, Vijaya Sharvani, Alpana Dubey, Meenakshi D’Souza, Nitish Bhardwaj, Sakshi Jain, Veenu Arora

Erschienen in: Artificial Intelligence in Education

Verlag: Springer International Publishing

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Abstract

Compile-time errors can wreak havoc for programmers – 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.

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Metadaten
Titel
RepairNet: Contextual Sequence-to-Sequence Network for Automated Program Repair
verfasst von
Kumar Abhinav
Vijaya Sharvani
Alpana Dubey
Meenakshi D’Souza
Nitish Bhardwaj
Sakshi Jain
Veenu Arora
Copyright-Jahr
2021
Buch
Artificial Intelligence in Education

Print ISBN: 978-3-030-78291-7

Electronic ISBN: 978-3-030-78292-4

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-78292-4_1

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