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Bugs are essential in software engineering; many research studies in the past decades have been proposed to detect, localize, and repair bugs in software systems.
Binary codes capable of correcting deletions, insertions, and reversals
V. I. Levenshtein et al · 1966
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Bugbench: Benchmarks for evaluating bug detection tools
S. Lu, Z. Li, F. Qin, L. Tan, P. Zhou, and Y. Zhou · 2005
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Higher order mutation testing
Y. Jia and M. Harman · 2008
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The national vulnerability database (nvd): Overview
H. Booth, D. Rike, and G. A. Witte · 2013
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Corebench: studying complexity of regression errors
M. Böhme and A. Roychoudhury · 2014
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The major mutation framework: efficient and scalable mutation analysis for java
R. Just · 2014
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Defects4j: a database of existing faults to enable controlled testing studies for java programs
R. Just, D. Jalali, and M. D. Ernst · 2014
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Deep learning for just-in-time defect prediction
X. Yang, D. Lo, X. Xia, Y. Zhang, and J. Sun · 2015
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Pit: a practical mutation testing tool for java (demo)
H. Coles, T. Laurent, C. Henard, M. Papadakis, and A. Ventresque · 2016
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The care and feeding of wild-caught mutants
D. B. Brown, M. Vaughn, B. Liblit, and T. Reps · 2017
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Beam search strategies for neural machine translation
M. Freitag and Y. Al-Onaizan · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin · 2017
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Enlightened debugging
X. Li, S. Zhu, M. d’Amorim, and A. Orso · 2018
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Bugs.jar: a large-scale, diverse dataset of real-world java bugs
R. K. Saha, Y. Lyu, W. Lam, H. Yoshida, and M. R. Prasad · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2019
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Codesearchnet challenge: Evaluating the state of semantic code search
H. Husain, H.-H. Wu, T. Gazit, M. Allamanis, and M. Brockschmidt · 2019
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Bugswarm: Mining and continuously growing a dataset of reproducible failures and fixes
D. A. Tomassi, N. Dmeiri, Y. Wang, A. Bhowmick, Y.-C. Liu, P. T. Devanbu, B. Vasilescu, and C. Rubio-González · 2019
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Learning how to mutate source code from bug-fixes
M. Tufano, C. Watson, G. Bavota, M. Di Penta, M. White, and D. Poshyvanyk · 2019
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A c/c++ code vulnerability dataset with code changes and cve summaries
J. Fan, Y. Li, S. Wang, and T. N. Nguyen · 2020
Cited alongside, same era.
CodeBERT: A pre-trained model for programming and natural languages
Z. Feng, D. Guo, D. Tang, N. Duan, X. Feng, M. Gong, L. Shou, B. Qin, T. Liu, D. Jiang, and M. Zhou · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu · 2020
Cited alongside, same era.
Codebleu: a method for automatic evaluation of code synthesis
S. Ren, D. Guo, S. Lu, L. Zhou, S. Liu, D. Tang, N. Sundaresan, M. Zhou, A. Blanco, and S. Ma · 2020
Cited alongside, same era.
In-context example selection with influences
T. Nguyen and E. Wong · 2023
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Vulgen: Realistic vulnerability generation via pattern mining and deep learning
Y. Nong, Y. Ou, M. Pradel, F. Chen, and H. Cai · 2023
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Towards efficient fine-tuning of pre-trained code models: An experimental study and beyond
E. Shi, Y. Wang, H. Zhang, L. Du, S. Han, D. Zhang, and H. Sun · 2023
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Stanford alpaca: An instruction-following llama model
R. Taori, I. Gulrajani, T. Zhang, Y. Dubois, X. Li, C. Guestrin, P. Liang, and T. B. Hashimoto · 2023
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Learning to construct better mutation faults
Z. Tian, J. Chen, Q. Zhu, J. Yang, and L. Zhang · 2023
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The plastic surgery hypothesis in the era of large language models
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Hazards of data leakage in machine learning: a study on classification of breast cancer using deep neural networks
R. K. Samala, H.-P. Chan, L. Hadjiiski, and S. Koneru · 2020
Cited alongside, same era.
What it would take to use mutation testing in industry—a study at facebook
M. Beller, C.-P. Wong, J. Bader, A. Scott, M. Machalica, S. Chandra, and E. Meijer · 2021
Cited alongside, same era.
Cvefixes: automated collection of vulnerabilities and their fixes from open-source software
G. Bhandari, A. Naseer, and L. Moonen · 2021
Cited alongside, same era.
Semantic bug seeding: a learning-based approach for creating realistic bugs
J. Patra and M. Pradel · 2021
Cited alongside, same era.
CodeT5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation
Y. Wang, W. Wang, S. Joty, and S. C. Hoi · 2021
Cited alongside, same era.
Natgen: generative pre-training by “naturalizing” source code
S. Chakraborty, T. Ahmed, Y. Ding, P. T. Devanbu, and B. Ray · 2022
Cited alongside, same era.
Perfect is the enemy of test oracle
A. R. Ibrahimzada, Y. Varli, D. Tekinoglu, and R. Jabbarvand · 2022
Cited alongside, same era.
C. S. Xia, Y. Ding, and L. Zhang · 2023
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Neurosymbolic repair of test flakiness
Y. Chen and R. Jabbarvand · 2024
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Codemind: Evaluating large language models for code reasoning
C. Liu, Y. Chen, and R. Jabbarvand · 2024
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Lost in translation: A study of bugs introduced by large language models while translating code
R. Pan, A. R. Ibrahimzada, R. Krishna, D. Sankar, L. P. Wassi, M. Merler, B. Sobolev, R. Pavuluri, S. Sinha, and R. Jabbarvand · 2024
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https://cloud.google.com/bigquery/public-data , 2025
Bigquery dataset · 2025
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Open ai chatgpt
O. AI · 2025
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Bugsinpy: Dataset of real-world python bugs
BugsInPy · 2025
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Github copilot
GitHub · 2025
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Manysstubs4j dataset
M. Group · 2025
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Alphatrans: A neuro-symbolic compositional approach for repository-level code translation and validation
A. R. Ibrahimzada, K. Ke, M. Pawagi, M. S. Abid, R. Pan, S. Sinha, and R. Jabbarvand · 2025
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Bugfarm artifact website
I. C. Lab · 2025
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Meta llama
Meta · 2025
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