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We consider the problem of learning to repair programs from diagnostic feedback (e.g., compiler error messages).
Long short-term memory
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Debugging: finding, fixing and flailing, a multi-institutional study of novice debuggers
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Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.-A · 2008
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Why does unsupervised pre-training help deep learning?
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On the difficulty of training recurrent neural networks
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Defects4j: A database of existing faults to enable controlled testing studies for java programs
Just, R., Jalali, D., and Ernst, M. D · 2014
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Programmers’ build errors: A case study at google
Seo, H., Sadowski, C., Elbaum, S., Aftandilian, E., and Bowdidge, R · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2015
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sk_p: a neural program corrector for moocs
Pu, Y., Narasimhan, K., Solar-Lezama, A., and Barzilay, R · 2016
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Deepfix: Fixing common c language errors by deep learning
Gupta, R., Pal, S., Kanade, A., and Shevade, S · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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Automatic grading and feedback using program repair for introductory programming courses
Parihar, S., Dadachanji, Z., Praveen Kumar Singh, R. D., Karkare, A., and Bhattacharya, A · 2017
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Get to the point: Summarization with pointer-generator networks
See, A., Liu, P. J., and Manning, C. D · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
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Graph-based neural multi-document summarization
Yasunaga, M., Zhang, R., Meelu, K., Pareek, A., Srinivasan, K., and Radev, D. R · 2017
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Compilation error repair: for the student programs, from the student programs
Ahmed, U. Z., Kumar, P., Karkare, A., Kar, P., and Gulwani, S · 2018
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Learning to represent programs with graphs
Allamanis, M., Brockschmidt, M., and Khademi, M · 2018
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Dialogue learning with human teaching and feedback in end-to-end trainable task-oriented dialogue systems
Liu, B., Tür, G., Hakkani-Tür, D., Shah, P., and Heck, L · 2018
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The living review on automated program repair
Monperrus, M · 2018
Sequencer: Sequence-to-sequence learning for end-to-end program repair
Chen, Z., Kommrusch, S. J., Tufano, M., Pouchet, L.-N., Poshyvanyk, D., and Monperrus, M · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
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Samplefix: Learning to correct programs by sampling diverse fixes
Hajipour, H., Bhattacharya, A., and Fritz, M · 2019
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Spoc: Search-based pseudocode to code
Kulal, S., Pasupat, P., Chandra, K., Lee, M., Padon, O., Aiken, A., and Liang, P · 2019
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Learning to repair compilation errors
Mesbah, A., Rice, A., Johnston, E., Glorioso, N., and Aftandilian, E · 2019
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Learning to fix build errors with graph2diff neural networks
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Deep contextualized word representations
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Deepbugs: a learning approach to name-based bug detection
Pradel, M. and Sen, K · 2018
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Graph attention networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
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Dynamic neural program embeddings for program repair
Wang, K., Singh, R., and Su, Z · 2018
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Graph convolution over pruned dependency trees improves relation extraction
Zhang, Y., Qi, P., and Manning, C. D · 2018
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Getafix: Learning to fix bugs automatically
Bader, J., Scott, A., Pradel, M., and Chandra, S · 2019
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Tarlow, D., Moitra, S., Rice, A., Chen, Z., Manzagol, P.-A., Sutton, C., and Aftandilian, E · 2019
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Neural program repair by jointly learning to localize and repair
Vasic, M., Kanade, A., Maniatis, P., Bieber, D., and Singh, R · 2019
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How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
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Neural networks for modeling source code edits
Zhao, R., Bieber, D., Swersky, K., and Tarlow, D · 2019
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Hoppity: Learning graph transformations to detect and fix bugs in programs
Dinella, E., Dai, H., Li, Z., Naik, M., Song, L., and Wang, K · 2020
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Codebert: A pre-trained model for programming and natural languages
Feng, Z., Guo, D., Tang, D., Duan, N., Feng, X., Gong, M., Shou, L., Qin, B., Liu, T., Jiang, D., and Zhou, M · 2020
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Pre-training graph neural networks
Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., and Leskovec, J · 2020
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Semantic scaffolds for pseudocode-to-code generation
Zhong, R., Stern, M., and Klein, D · 2020
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