Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 209

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

DLFix: context-based code transformation learning for automated program repair

Li, Yi, Wang, Shaohua, Nguyen, Tien N.·2020·Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering
DOI 10.1145/3377811.3380345
Open this paper in CoChat — free account

Abstract

Automated Program Repair (APR) is very useful in helping developers in the process of software development and maintenance. Despite recent advances in deep learning (DL), the DL-based APR approaches still have limitations in learning bug-fixing code changes and the context of the surrounding source code of the bug-fixing code changes. These limitations lead to incorrect fixing locations or fixes. In this paper, we introduce DLFix, a two-tier DL model that treats APR as code transformation learning from the prior bug fixes and the surrounding code contexts of the fixes. The first layer is a tree-based RNN model that learns the contexts of bug fixes and its result is used as an additional weighting input for the second layer designed to learn the bug-fixing code transformations.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1145/3377811.3380345

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 3 references

Research works printed but not linked: 42

  1. 2019. The GitHub Repository for This Study. https://github.com/ICSE-2019-AUTOFIX/ICSE-2019-AUTOFIX
  2. 10.1109/PRDC.2006.18 · DOI · Paper page
  3. Denny Britz, Anna Goldie, Minh-Thang Luong, and Quoc Le. 2017. Massive exploration of neural machine translation architectures. arXiv preprint arXiv:1703.03906 (2017). · arXiv
  4. Saikat Chakraborty, Miltiadis Allamanis, and Baishakhi Ray. 2018. CODIT: Code Editing with Tree-Based Neural Machine Translation. arXiv preprint arXiv:1810.00314 (2018). · arXiv
  5. 10.1109/TSE.2019.2940179 · DOI · Paper page
  6. Yann Le Cun, Conrad C. Galland, and Geoffrey E. Hinton. 1989. GEMINI: Gradient Estimation through Matrix Inversion after Noise Injection. In Advances in Neural Information Processing Systems 1. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 141--148.
  7. 10.1109/ICSE.2012.6227211 · DOI · Paper page
  8. 10.1109/TSE.2011.104 · DOI · Paper page
  9. 10.1609/aaai.v31i1.10742 · DOI · Paper page
  10. Hideaki Hata, Emad Shihab, and Graham Neubig. 2018. Learning to generate corrective patches using neural machine translation. arXiv preprint arXiv:1812.07170 (2018). · arXiv
  11. 10.1109/ICSE.2013.6606626 · DOI · Paper page
  12. 10.1109/ICSE.2013.6606626 · DOI · Paper page
  13. Yoon Kim. 2014. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014). · arXiv
  14. Anil Koyuncu, Kui Liu, Tegawendé F Bissyandé, Dongsun Kim, Jacques Klein, Martin Monperrus, and Yves Le Traon. 2018. Fixminer: Mining relevant fix patterns for automated program repair. arXiv preprint arXiv:1810.01791 (2018). · arXiv
  15. 10.1109/SANER.2016.76 · DOI · Paper page
  16. 10.1145/3360588 · DOI · Paper page
  17. 10.1109/APSEC.2018.00085 · DOI
  18. 10.1109/ICST.2019.00020 · DOI
  19. 10.1109/SANER2019.8667970 · doi:10.1109/saner2019.8667970
  20. 10.1145/3293882.3330577 · DOI · Paper page
  21. 10.1145/3106237.3106253 · DOI · Paper page
  22. 10.1145/2786805.2786811 · DOI · Paper page
  23. 10.1145/2837614.2837617 · DOI · Paper page
  24. 10.1145/2931037.2948705 · DOI · Paper page
  25. 10.1109/ICSE.2013.6606623 · DOI · Paper page
  26. 10.1145/1806799.1806847 · DOI
  27. 10.1109/ICSE.2017.62 · DOI · Paper page
  28. 10.1145/2568225.2568254 · DOI · Paper page
  29. 10.1145/2393596.2393659 · DOI
  30. Ripon K. Saha, Yingjun Lyu, Wing Lam, Hiroaki Yoshida, and Mukul R. Prasad. 2018. Bugs.jar: A Large-Scale, Diverse Dataset of Real-World Java Bugs. In Proceedings of the 15th IEEE/ACM International Conference on Mining Software Repositories (MSR). 10--13.
  31. 10.1109/ASE.2017.8115675 · DOI · Paper page
  32. 10.1109/ICSE.2019.00020 · DOI
  33. Kai Sheng Tai, Richard Socher, and Christopher D Manning. 2015. Improved semantic representations from tree-structured long short-term memory networks. arXiv preprint arXiv:1503.00075 (2015). · arXiv
  34. 10.1145/2950290.2950295 · DOI
  35. 10.1109/ICSE.2019.00021 · DOI · Paper page
  36. 10.1145/3238147.3240732 · DOI
  37. 10.1145/3238147.3238206 · DOI · Paper page
  38. 10.1145/3180155.3180233 · DOI · Paper page
  39. 10.1109/SANER.2019.8668043 · DOI · Paper page
  40. 10.1145/2970276.2970326 · DOI · Paper page
  41. Qi Xin and Steven P. Reiss. 2017. Leveraging Syntax-Related Code for Automated Program Repair. In Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering (Urbana-Champaign, IL, USA) (ASE 2017). IEEE Press, 660--670.
  42. 10.1109/ICSE.2017.45 · DOI · Paper page

Cited by

See all 209

Metadata

Authors
Li, Y., Wang, S., & Nguyen, T. N.
Venue
Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering
Publisher
ACM
Published
2020-06-27
Pages
602-614
DOI
10.1145/3377811.3380345
OpenAlex
W3089621332
Cited by
209
References
3
FWCI
20.19
Type
conference paper
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Funders
US National Science foundation (NSF) (CCF-1723215, CCF-1723432, TWC-1723198, CCF-1518897, CCF-1513263)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: DLFix: context-based code transformation learning for automated program repair. https://app.cochat.ai/papers/10.1145/3377811.3380345
The paper
Li, Y., Wang, S., & Nguyen, T. N. (2020). DLFix: context-based code transformation learning for automated program repair. Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering. https://doi.org/10.1145/3377811.3380345

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.