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A fundamental skill among human developers is the ability to understand and reason about program execution.
The pragmatic programmer: From journeyman to master
A. Hunt and D. Thomas · 1999
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A. Graves, G. Wayne, and I. Danihelka · 2014
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Studying the advancement in debugging practice of professional software developers
B. Siegmund, M. Perscheid, M. Taeumel, and R. Hirschfeld · 2014
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W. Zaremba and I. Sutskever · 2014
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Programming with a differentiable Forth interpreter
M. Bosnjak, T. Rocktäschel, J. Naradowsky, and S. Riedel · 2016
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Differentiable programs with neural libraries
A. L. Gaunt, M. Brockschmidt, N. Kushman, and D. Tarlow · 2016
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Thinking fast and slow with deep learning and tree search
T. W. Anthony, Z. Tian, and D. Barber · 2017
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SequenceR: Sequence-to-sequence learning for end-to-end program repair
Z. Chen, S. Kommrusch, M. Tufano, L.-N. Pouchet, D. Poshyvanyk, and M. Martin · 2018
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Deep code comment generation
X. Hu, G. Li, X. Xia, D. Lo, and Z. Jin · 2018
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Memory augmented policy optimization for program synthesis and semantic parsing
C. Liang, M. Norouzi, J. Berant, Q. V. Le, and N. Lao · 2018
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Improving neural program synthesis with inferred execution traces
E. C. Shin, I. Polosukhin, and D. Song · 2018
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Robust text-to-SQL generation with execution-guided decoding
C. Wang, K. Tatwawadi, M. Brockschmidt, P.-S. Huang, Y. Mao, O. Polozov, and R. Singh · 2018
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Iterative search for weakly supervised semantic parsing
P. Dasigi, M. Gardner, S. Murty, L. Zettlemoyer, and E. H. Hovy · 2019
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SPoC: Search-based pseudocode to code
S. Kulal, P. Pasupat, K. Chandra, M. Lee, O. Padon, A. Aiken, and P. S. Liang · 2019
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Automated program repair
C. Le Goues, M. Pradel, and A. Roychoudhury · 2019
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Explain yourself! leveraging language models for commonsense reasoning
N. F. Rajani, B. McCann, C. Xiong, and R. Socher · 2019
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Learning to execute programs with instruction pointer attention graph neural networks
D. Bieber, C. Sutton, H. Larochelle, and D. Tarlow · 2020
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Synthesize, execute and debug: Learning to repair for neural program synthesis
K. Gupta, P. E. Christensen, X. Chen, and D. X. Song · 2020
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DLFix: Context-based code transformation learning for automated program repair
Y. Li, S. Wang, and T. N. Nguyen · 2020
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Merging weak and active supervision for semantic parsing
A. Ni, P. Yin, and G. Neubig · 2020
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Unsupervised commonsense question answering with self-talk
V. Shwartz, P. West, R. Le Bras, C. Bhagavatula, and Y. Choi · 2020
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Program synthesis with large language models
J. Austin, A. Odena, M. Nye, M. Bosma, H. Michalewski, D. Dohan, E. Jiang, C. Cai, M. Terry, Q. Le, et al · 2021
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Show your work: Scratchpads for intermediate computation with language models
M. Nye, A. J. Andreassen, G. Gur-Ari, H. Michalewski, J. Austin, D. Bieber, D. Dohan, A. Lewkowycz, M. Bosma, D. Luan, et al · 2021
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D. Bieber, R. Goel, D. Zheng, H. Larochelle, and D. Tarlow · 2022
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Scaling instruction-finetuned language models
H. W. Chung, L. Hou, S. Longpre, B. Zoph, Y. Tay, W. Fedus, E. Li, X. Wang, M. Dehghani, S. Brahma, A. Webson, S. S. Gu, Z. Dai, M. Suzgun, X. Chen, A. Chowdhery, D. Valter, S. Narang, G. Mishra, A. W. Yu, V. Zhao, Y. Huang, A. M. Dai, H. Yu, S. Petrov, E. H. hsin Chi, J. Dean, J. Devlin, A. Roberts, D. Zhou, Q. V. Le, and J. Wei · 2022
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Counterfactual explanations for models of code
J. Cito, I. Dillig, V. Murali, and S. Chandra · 2022
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Automated repair of programs from large language models
Z. Fan, X. Gao, M. Mirchev, A. Roychoudhury, and S. H. Tan · 2022
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Synthesizing research on programmers’ mental models of programs, tasks and concepts - a systematic literature review
A. Heinonen, B. Lehtelä, A. Hellas, and F. Fagerholm · 2022
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Automating code review activities by large-scale pre-training
Z. Li, S. Lu, D. Guo, N. Duan, S. Jannu, G. Jenks, D. Majumder, J. Green, A. Svyatkovskiy, S. Fu, and N. Sundaresan · 2022
The flan collection: Designing data and methods for effective instruction tuning
S. Longpre, L. Hou, T. Vu, A. Webson, H. W. Chung, Y. Tay, D. Zhou, Q. V. Le, B. Zoph, J. Wei, and A. Roberts · 2023
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ChatGPT: Understanding code syntax and semantics
W. Ma, S. Liu, W. Wang, Q. Hu, Y. Liu, C. Zhang, L. Nie, and Y. Liu · 2023
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Self-refine: Iterative refinement with self-feedback
A. Madaan, N. Tandon, P. Gupta, S. Hallinan, L. Gao, S. Wiegreffe, U. Alon, N. Dziri, S. Prabhumoye, Y. Yang, S. Welleck, B. P. Majumder, S. Gupta, A. Yazdanbakhsh, and P. Clark · 2023
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Orca 2: Teaching small language models how to reason
A. Mitra, L. D. Corro, S. Mahajan, A. Codas, C. Simoes, S. Agrawal, X. Chen, A. Razdaibiedina, E. Jones, K. Aggarwal, H. Palangi, G. Zheng, C. Rosset, H. Khanpour, and A. Awadallah · 2023
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Learning math reasoning from self-sampled correct and partially-correct solutions
A. Ni, J. P. Inala, C. Wang, A. Polozov, C. Meek, D. Radev, and J. Gao · 2022
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CrossBeam: Learning to search in bottom-up program synthesis
K. Shi, H. Dai, K. Ellis, and C. Sutton · 2022
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Solving math word problems with process- and outcome-based feedback
J. Uesato, N. Kushman, R. Kumar, F. Song, N. Siegel, L. Wang, A. Creswell, G. Irving, and I. Higgins · 2022
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Less training, more repairing please: revisiting automated program repair via zero-shot learning
C. Xia and L. Zhang · 2022
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ReAct: Synergizing reasoning and acting in language models
S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y. Cao · 2022
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SelfAPR: Self-supervised program repair with test execution diagnostics
H. Ye, M. Martinez, X. Luo, T. Zhang, and M. Martin · 2022
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STaR: Bootstrapping reasoning with reasoning
E. Zelikman, Y. Wu, J. Mu, and N. Goodman · 2022
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N. Muennighoff, Q. Liu, A. Zebaze, Q. Zheng, B. Hui, T. Y. Zhuo, S. Singh, X. Tang, L. Von Werra, and S. Longpre · 2023
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Orca: Progressive learning from complex explanation traces of GPT-4
S. Mukherjee, A. Mitra, G. Jawahar, S. Agarwal, H. Palangi, and A. H. Awadallah · 2023
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LEVER: Learning to verify language-to-code generation with execution
A. Ni, S. Iyer, D. Radev, V. Stoyanov, W.-t. Yih, S. Wang, and X. V. Lin · 2023
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Demystifying GPT self-repair for code generation
T. X. Olausson, J. P. Inala, C. Wang, J. Gao, and A. Solar-Lezama · 2023
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GPT-4 technical report, 2023
OpenAI · 2023
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Enhancing automated program repair through fine-tuning and prompt engineering
R. Paul, M. M. Hossain, M. L. Siddiq, M. Hasan, A. Iqbal, and J. C. S. Santos · 2023
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Can large language models reason about program invariants?
K. Pei, D. Bieber, K. Shi, C. Sutton, and P. Yin · 2023
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Receval: Evaluating reasoning chains via correctness and informativeness
A. Prasad, S. Saha, X. Zhou, and M. Bansal · 2023
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The programmer’s assistant: Conversational interaction with a large language model for software development
S. I. Ross, F. Martinez, S. Houde, M. J. Muller, and J. D. Weisz · 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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An analysis of the automatic bug fixing performance of ChatGPT
D. Sobania, M. Briesch, C. Hanna, and J. Petke · 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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Keep the conversation going: Fixing 162 out of 337 bugs for $0.42 each using ChatGPT
C. Xia and L. Zhang · 2023
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Automated program repair in the era of large pre-trained language models
C. Xia, Y. Wei, and L. Zhang · 2023
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Natural language to code generation in interactive data science notebooks
P. Yin, W.-D. Li, K. Xiao, A. Rao, Y. Wen, K. Shi, J. Howland, P. Bailey, M. Catasta, H. Michalewski, O. Polozov, and C. Sutton · 2023
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DeepSeek-Coder: When the large language model meets programming – the rise of code intelligence, 2024
D. Guo, Q. Zhu, D. Yang, Z. Xie, K. Dong, W. Zhang, G. Chen, X. Bi, Y. Wu, Y. K. Li, F. Luo, Y. Xiong, and W. Liang · 2024
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A. Q. Jiang, A. Sablayrolles, A. Roux, A. Mensch, B. Savary, C. Bamford, D. S. Chaplot, D. d. l. Casas, E. B. Hanna, F. Bressand, et al · 2024
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ExeDec: Execution decomposition for compositional generalization in neural program synthesis
K. Shi, J. Hong, Y. Deng, P. Yin, M. Zaheer, and C. Sutton · 2024
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Math-shepherd: Verify and reinforce llms step-by-step without human annotations
P. Wang, L. Li, Z. Shao, R. X. Xu, D. Dai, Y. Li, D. Chen, Y. Wu, and Z. Sui · 2024
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