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International Conference on Artificial Intelligence in Education

AIED 2021: Artificial Intelligence in Education pp 3–15Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12748))

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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Author information

Authors and Affiliations

  1. Accenture Labs, Bangalore, India

    Kumar Abhinav, Alpana Dubey, Nitish Bhardwaj, Sakshi Jain & Veenu Arora

  2. IIIT, Bangalore, India

    Vijaya Sharvani & Meenakshi D’Souza

Authors
  1. Kumar Abhinav
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  2. Vijaya Sharvani
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  3. Alpana Dubey
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  4. Meenakshi D’Souza
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  5. Nitish Bhardwaj
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  6. Sakshi Jain
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  7. Veenu Arora
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Corresponding author

Correspondence to Alpana Dubey .

Editor information

Editors and Affiliations

  1. Technion – Israel Institute of Technology, Haifa, Israel

    Ido Roll

  2. Arizona State University, Tempe, AZ, USA

    Danielle McNamara

  3. Utrecht University, Utrecht, The Netherlands

    Sergey Sosnovsky

  4. London Knowledge Lab, London, UK

    Rose Luckin

  5. University of Leeds, Leeds, UK

    Vania Dimitrova

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Abhinav, K. et al. (2021). RepairNet: Contextual Sequence-to-Sequence Network for Automated Program Repair. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12748. Springer, Cham. https://doi.org/10.1007/978-3-030-78292-4_1

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78291-7

  • Online ISBN: 978-3-030-78292-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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