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Machine learning-based program analyses have recently shown the promise of integrating formal and probabilistic reasoning towards aiding software development.
Generative adversarial networks
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Are mutants a valid substitute for real faults in software testing?
R. Just, D. Jalali, L. Inozemtseva, M. D. Ernst, R. Holmes, and G. Fraser · 2014
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Predicting program properties from Big Code
V. Raychev, M. Vechev, and A. Krause · 2015
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Gated graph sequence neural networks
Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel · 2016
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On the ”naturalness” of buggy code
B. Ray, V. Hellendoorn, S. Godhane, Z. Tu, A. Bacchelli, and P. Devanbu · 2016
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Bugram: bug detection with n-gram language models
S. Wang, D. Chollak, D. Movshovitz-Attias, and L. Tan · 2016
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Neural message passing for quantum chemistry
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl · 2017
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Pointer sentinel mixture models
S. Merity, C. Xiong, J. Bradbury, and R. Socher · 2017
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Automatic differentiation in PyTorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
Earlier work this paper cites.
Detecting argument selection defects
A. Rice, E. Aftandilian, C. Jaspan, E. Johnston, M. Pradel, and Y. Arroyo-Paredes · 2017
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Deep learning on code with an unbounded vocabulary
M. Cvitkovic, B. Singh, and A. Anandkumar · 2018
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DeepBugs: A learning approach to name-based bug detection
M. Pradel and K. Sen · 2018
Cited alongside, same era.
PyDriller: Python framework for mining software repositories
D. Spadini, M. Aniche, and A. Bacchelli · 2018
Cited alongside, same era.
The adverse effects of code duplication in machine learning models of code
M. Allamanis · 2019
Cited alongside, same era.
Hoppity: Learning graph transformations to detect and fix bugs in programs
E. Dinella, H. Dai, Z. Li, M. Naik, L. Song, and K. Wang · 2019
Cited alongside, same era.
Unsupervised learning of API aliasing specifications
J. Eberhardt, S. Steffen, V. Raychev, and M. Vechev · 2019
Cited alongside, same era.
Neural program repair by jointly learning to localize and repair
M. Vasic, A. Kanade, P. Maniatis, D. Bieber, and R. Singh · 2019
Global relational models of source code
V. J. Hellendoorn, C. Sutton, R. Singh, P. Maniatis, and D. Bieber · 2020
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Learning and evaluating contextual embedding of source code
A. Kanade, P. Maniatis, G. Balakrishnan, and K. Shi · 2020
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How often do single-statement bugs occur? the ManySStuBs4J dataset
R.-M. Karampatsis and C. Sutton · 2020
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Libraries.io Open Source Repository and Dependency Metadata, Jan. 2020
J. Katz · 2020
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Semantic robustness of models of source code
G. Ramakrishnan, J. Henkel, Z. Wang, A. Albarghouthi, S. Jha, and T. Reps · 2020
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Learning to fix build errors with Graph2Diff neural networks
D. Tarlow, S. Moitra, A. Rice, Z. Chen, P.-A. Manzagol, C. Sutton, and E. Aftandilian · 2020
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Cited alongside, same era.
Typilus: Neural type hints
M. Allamanis, E. Barr, S. Ducousso, and Z. Gao · 2020
Cited alongside, same era.
Rezero is all you need: Fast convergence at large depth
T. Bachlechner, B. P. Majumder, H. H. Mao, G. W. Cottrell, and J. McAuley · 2020
Cited alongside, same era.
ELECTRA: Pre-training text encoders as discriminators rather than generators
K. Clark, M.-T. Luong, Q. V. Le, and C. D. Manning · 2020
Cited alongside, same era.
Emergent complexity and zero-shot transfer via unsupervised environment design
M. Dennis, N. Jaques, E. Vinitsky, A. Bayen, S. Russell, A. Critch, and S. Levine · 2020
Cited alongside, same era.
A survey of machine learning for big code and naturalness
M. Allamanis, E. T. Barr, P. Devanbu, and C. Sutton
Cited in the paper.
Learning to represent programs with graphs
M. Allamanis, M. Brockschmidt, and M. Khademi
Cited in the paper.
Later among the works it cites.
Blended, precise semantic program embeddings
K. Wang and Z. Su · 2020
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LambdaNet: Probabilistic type inference using graph neural networks
J. Wei, M. Goyal, G. Durrett, and I. Dillig · 2020
Later among the works it cites.
Semantic bug seeding: a learning-based approach for creating realistic bugs
J. Patra and M. Pradel · 2021
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Going deeper with image transformers
H. Touvron, M. Cord, A. Sablayrolles, G. Synnaeve, and H. Jégou · 2021
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