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Software defects are an inherent part of software development and maintenance.
Language models are few-shot learners,
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Sequencer: Sequence-to-sequence learning for end-to-end program repair,
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A fast and elitist multiobjective genetic algorithm: Nsga-ii,
K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, · 2002
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An evaluation of similarity coefficients for software fault localization,
R. Abreu, P. Zoeteweij, A. J. Van Gemund, · 2006
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On the accuracy of spectrum-based fault localization,
R. Abreu, P. Zoeteweij, A. J. Van Gemund, · 2007
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Predicting faults from cached history,
S. Kim, T. Zimmermann, E. J. Whitehead Jr, A. Zeller, · 2007
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A genetic programming approach to automated software repair,
S. Forrest, T. Nguyen, W. Weimer, C. Le Goues, · 2009
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Genprog: A generic method for automatic software repair,
C. Le Goues, T. Nguyen, S. Forrest, W. Weimer, · 2011
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Where should the bugs be fixed? more accurate information retrieval-based bug localization based on bug reports,
J. Zhou, H. Zhang, D. Lo, · 2012
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A systematic study of automated program repair: Fixing 55 out of 105 bugs for $8 each,
C. Le Goues, M. Dewey-Vogt, S. Forrest, W. Weimer, · 2012
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Gzoltar: an eclipse plug-in for testing and debugging,
J. Campos, A. Riboira, A. Perez, R. Abreu, · 2012
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Automatic patch generation learned from human-written patches,
D. Kim, J. Nam, J. Song, S. Kim, · 2013
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Do the fix ingredients already exist? an empirical inquiry into the redundancy assumptions of program repair approaches,
M. Martinez, W. Weimer, M. Monperrus, · 2014
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The strength of random search on automated program repair,
Y. Qi, X. Mao, Y. Lei, Z. Dai, C. Wang, · 2014
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Defects4j: A database of existing faults to enable controlled testing studies for java programs,
R. Just, D. Jalali, M. D. Ernst, · 2014
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Sequence to sequence learning with neural networks,
I. Sutskever, O. Vinyals, Q. V. Le, · 2014
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Metallaxis-fl: mutation-based fault localization,
M. Papadakis, Y. Le Traon, · 2015
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Neural machine translation of rare words with subword units,
R. Sennrich, B. Haddow, A. Birch, · 2015
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Nopol: Automatic repair of conditional statement bugs in java programs,
J. Xuan, M. Martinez, F. Demarco, M. Clement, S. L. Marcote, T. Durieux, D. Le Berre, M. Monperrus, · 2016
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Angelix: Scalable multiline program patch synthesis via symbolic analysis,
S. Mechtaev, J. Yi, A. Roychoudhury, · 2016
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Transforming programs and tests in tandem for fault localization,
X. Li, L. Zhang, · 2017
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Genetic improvement of software: a comprehensive survey,
J. Petke, S. O. Haraldsson, M. Harman, W. B. Langdon, D. R. White, J. R. Woodward, · 2017
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Attention is all you need,
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, I. Polosukhin, · 2017
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Quixbugs: A multi-lingual program repair benchmark set based on the quixey challenge,
D. Lin, J. Koppel, A. Chen, A. Solar-Lezama, · 2017
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Automatic software repair: A survey,
L. Gazzola, D. Micucci, L. Mariani, · 2018
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Automatic software repair: A bibliography,
M. Monperrus, · 2018
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Arja: Automated repair of java programs via multi-objective genetic programming,
Y. Yuan, W. Banzhaf, · 2018
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Towards practical program repair with on-demand candidate generation,
J. Hua, M. Zhang, K. Wang, S. Khurshid, · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding,
J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, · 2018
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Evaluating representation learning of code changes for predicting patch correctness in program repair,
H. Tian, K. Liu, A. K. Kaboré, A. Koyuncu, L. Li, J. Klein, T. F. Bissyandé, · 2020
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Y. Wang, W. Wang, S. Joty, S. C. Hoi, · 2021
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A syntax-guided edit decoder for neural program repair,
Q. Zhu, Z. Sun, Y.-a. Xiao, W. Zhang, K. Yuan, Y. Xiong, L. Zhang, · 2021
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Evaluating large language models trained on code,
M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. d. O. Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman, et al., · 2021
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Applying codebert for automated program repair of java simple bugs,
E. Mashhadi, H. Hemmati, · 2021
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
T. Kudo, J. Richardson, · 2018
Cited alongside, same era.
Shaping program repair space with existing patches and similar code,
J. Jiang, Y. Xiong, H. Zhang, Q. Gao, X. Chen, · 2018
Cited alongside, same era.
Improved representation and genetic operators for linear genetic programming for automated program repair,
V. P. L. Oliveira, E. F. d. Souza, C. L. Goues, C. G. Camilo-Junior, · 2018
Cited alongside, same era.
Accelerating search-based program repair,
B. Mehne, H. Yoshida, M. R. Prasad, K. Sen, D. Gopinath, S. Khurshid, · 2018
Cited alongside, same era.
Search-based efficient automated program repair using mutation and fault localization,
S. Sun, J. Guo, R. Zhao, Z. Li, · 2018
Cited alongside, same era.
Context-aware patch generation for better automated program repair,
M. Wen, J. Chen, R. Wu, D. Hao, S.-C. Cheung, · 2018
Cited alongside, same era.
Bugs. jar: A large-scale, diverse dataset of real-world java bugs,
R. K. Saha, Y. Lyu, W. Lam, H. Yoshida, M. R. Prasad, · 2018
Cited alongside, same era.
Automated classification of overfitting patches with statically extracted code features,
H. Ye, J. Gu, M. Martinez, T. Durieux, M. Monperrus, · 2021
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Cure: Code-aware neural machine translation for automatic program repair,
N. Jiang, T. Lutellier, L. Tan, · 2021
Later among the works it cites.
Less training, more repairing please: revisiting automated program repair via zero-shot learning,
C. S. Xia, L. Zhang, · 2022
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Practical program repair in the era of large pre-trained language models,
C. S. Xia, Y. Wei, L. Zhang, · 2022
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Dear: A novel deep learning-based approach for automated program repair,
Y. Li, S. Wang, T. N. Nguyen, · 2022
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Incoder: A generative model for code infilling and synthesis,
D. Fried, A. Aghajanyan, J. Lin, S. Wang, E. Wallace, F. Shi, R. Zhong, W.-t. Yih, L. Zettlemoyer, M. Lewis, · 2022
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Context-aware code change embedding for better patch correctness assessment,
B. Lin, S. Wang, M. Wen, X. Mao, · 2022
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Impact of code language models on automated program repair,
N. Jiang, K. Liu, T. Lutellier, L. Tan, · 2023
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Code llama: Open foundation models for code,
B. Roziere, J. Gehring, F. Gloeckle, S. Sootla, I. Gat, X. E. Tan, Y. Adi, J. Liu, T. Remez, J. Rapin, et al., · 2023
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Gamma: Revisiting template-based automated program repair via mask prediction,
Q. Zhang, C. Fang, T. Zhang, B. Yu, W. Sun, Z. Chen, · 2023
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Llama 2: Open foundation and fine-tuned chat models,
H. Touvron, L. Martin, K. Stone, P. Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale, et al., · 2023
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Gamma: Revisiting template-based automated program repair via mask prediction,
Q. Zhang, C. Fang, T. Zhang, B. Yu, W. Sun, Z. Chen, · 2023
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Automated repair of declarative software specifications in the era of large language models,
M. R. Hasan, J. Li, I. Ahmed, H. Bagheri, · 2023
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Automated repair of programs from large language models,
Z. Fan, X. Gao, M. Mirchev, A. Roychoudhury, S. H. Tan, · 2023
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A survey of learning-based automated program repair,
Q. Zhang, C. Fang, Y. Ma, W. Sun, Z. Chen, · 2023
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Program repair competition 2024 (2024)
R. Shariffdeen, Y. Noller, M. Mirchev, H. Ruan, X. Gao, A. Costea, G. J. Duck, A. Roychoudhury, · 2024
Closest in time.
Roformer: Enhanced transformer with rotary position embedding,
J. Su, M. Ahmed, Y. Lu, S. Pan, W. Bo, Y. Liu, · 2024
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Appt: Boosting automated patch correctness prediction via fine-tuning pre-trained models,
Q. Zhang, C. Fang, W. Sun, Y. Liu, T. He, X. Hao, Z. Chen, · 2024
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Deepcode ai fix: Fixing security vulnerabilities with large language models,
B. Berabi, A. Gronskiy, V. Raychev, G. Sivanrupan, V. Chibotaru, M. Vechev, · 2024
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