Practical Program Repair via Preference-based Ensemble Strategy-Reference-Cited by-同舟云学术

Practical Program Repair via Preference-based Ensemble Strategy

Published:2024-02-06 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 46th IEEE/ACM International Conference on Software Engineering
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Author:

Zhong Wenkang¹^ORCID,Li Chuanyi¹^ORCID,Liu Kui²^ORCID,Xu Tongtong²^ORCID,Ge Jidong¹^ORCID,Bissyande Tegawende F.³^ORCID,Luo Bin¹^ORCID,Ng Vincent⁴^ORCID

Affiliation:

1. Nanjing University, National Key Laboratory for Novel Software Technology, Nanjing, China

2. Huawei Software Engineering Application Technology Lab, Hangzhou, China

3. University of Luxembourg, Luxembourg, Luxembourg

4. University of Texas at Dallas, Human Language Technology Research Institute, Richardson, Texas, USA

Funder

the National Key Research and Development Program of China

the National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province, China

CCF-Huawei Populus Grove Fund

NSF (National Science Foundation)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3597503.3623310

Reference53 articles.

1. E-APR: Mapping the effectiveness of automated program repair techniques

2. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural Machine Translation by Jointly Learning to Align and Translate. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7--9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1409.0473

3. Daniel Balouek, Alexandra Carpen Amarie, Ghislain Charrier, Frédéric Desprez, Emmanuel Jeannot, Emmanuel Jeanvoine, Adrien Lèbre, David Margery, Nicolas Niclausse, Lucas Nussbaum, et al. 2012. Adding virtualization capabilities to the Grid'5000 testbed. In International Conference on Cloud Computing and Services Science. Springer, 3--20.

4. Saikat Chakraborty, Yangruibo Ding, Miltiadis Allamanis, and Baishakhi Ray. 2020. Codit: Code editing with tree-based neural models. IEEE Transactions on Software Engineering (2020).

5. Sequencer: Sequence-to-sequence learning for end-to-end program repair;Chen Zimin;IEEE Transactions on Software Engineering,2019