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Deep learning has recently achieved initial success in program analysis tasks such as bug detection.
Huggingface’s transformers: State-of-the-art natural language processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., and Brew, J · 1910
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E. Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 1912
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The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets
Saito, T. and Rehmsmeier, M · 2015
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A convolutional attention network for extreme summarization of source code
Allamanis, M., Peng, H., and Sutton, C · 2016
Earlier work this paper cites.
Probabilistic model for code with decision trees
Raychev, V., Bielik, P., and Vechev, M · 2016
Earlier work this paper cites.
Focal loss for dense object detection
Lin, T., Goyal, P., Girshick, R. B., He, K., and Dollár, P · 2017
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Detecting argument selection defects
Rice, A., Aftandilian, E., Jaspan, C., Johnston, E., Pradel, M., and Arroyo-Paredes, Y · 2017
Earlier work this paper cites.
Learning to represent programs with graphs
Allamanis, M., Brockschmidt, M., and Khademi, M · 2018
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Dynamic task prioritization for multitask learning
Guo, M., Haque, A., Huang, D., Yeung, S., and Fei-Fei, L · 2018
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Vuldeepecker: A deep learning-based system for vulnerability detection
Li, Z., Zou, D., Xu, S., Ou, X., Jin, H., Wang, S., Deng, Z., and Zhong, Y · 2018
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Deepbugs: a learning approach to name-based bug detection
Pradel, M. and Sen, K · 2018
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The adverse effects of code duplication in machine learning models of code
Allamanis, M · 2019
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code2vec: learning distributed representations of code
Alon, U., Zilberstein, M., Levy, O., and Yahav, E · 2019
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Generative code modeling with graphs
Brockschmidt, M., Allamanis, M., Gaunt, A. L., and Polozov, O · 2019
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BERT: pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M., Lee, K., and Toutanova, K · 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
Earlier work this paper cites.
Learning to represent edits
Yin, P., Neubig, G., Allamanis, M., Brockschmidt, M., and Gaunt, A. L · 2019
Cited alongside, same era.
Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks
Zhou, Y., Liu, S., Siow, J. K., Du, X., and Liu, Y · 2019
Cited alongside, same era.
Typilus: neural type hints
Allamanis, M., Barr, E. T., Ducousso, S., and Gao, Z · 2020
Cited alongside, same era.
Structural language models of code
Alon, U., Sadaka, R., Levy, O., and Yahav, E · 2020
Cited alongside, same era.
Adversarial robustness for code
Bielik, P. and Vechev, M · 2020
Cited alongside, same era.
A structural model for contextual code changes
Brody, S., Alon, U., and Yahav, E · 2020
Cited alongside, same era.
Program synthesis with large language models
Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C. J., Terry, M., Le, Q. V., and Sutton, C · 2021
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Tfix: Learning to fix coding errors with a text-to-text transformer
Berabi, B., He, J., Raychev, V., and Vechev, M · 2021
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Learning to find naming issues with big code and small supervision
He, J., Lee, C., Raychev, V., and Vechev, M · 2021
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Contrastive code representation learning
Jain, P., Jain, A., Zhang, T., Abbeel, P., Gonzalez, J., and Stoica, I · 2021
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WILDS: A benchmark of in-the-wild distribution shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., Lee, T., David, E., Stavness, I., Guo, W., Earnshaw, B., Haque, I., Beery, S. M., Leskovec, J., Kundaje, A., Pierson, E., Levine, S., Finn, C., and Liang, P · 2021
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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
Cited alongside, same era.
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
Cited alongside, same era.
Global relational models of source code
Hellendoorn, V. J., Sutton, C., Singh, R., Maniatis, P., and Bieber, D · 2020
Cited alongside, same era.
Self-supervised visual feature learning with deep neural networks: A survey
Jing, L. and Tian, Y · 2020
Cited alongside, same era.
Learning and evaluating contextual embedding of source code
Kanade, A., Maniatis, P., Balakrishnan, G., and Shi, K · 2020
Cited alongside, same era.
How often do single-statement bugs occur?: The manysstubs4j dataset
Karampatsis, R. and Sutton, C · 2020
Cited alongside, same era.
Neural program generation modulo static analysis
Mukherjee, R., Wen, Y., Chaudhari, D., Reps, T. W., Chaudhuri, S., and Jermaine, C · 2021
Later among the works it cites.
Semantic bug seeding: a learning-based approach for creating realistic bugs
Patra, J. and Pradel, M · 2021
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Break-it-fix-it: Unsupervised learning for program repair
Yasunaga, M. and Liang, P · 2021
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A survey on multi-task learning
Zhang, Y. and Yang, Q · 2021
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Language-agnostic representation learning of source code from structure and context
Zügner, D., Kirschstein, T., Catasta, M., Leskovec, J., and Günnemann, S · 2021
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URL https://github.com/google-research-datasets/eth_py150_open
ETH Py150 Open Corpus, 2022 · 2022
Closest in time.
URL https://stackoverflow.com/questions/3257919/what-is-the-difference-between-is-none-and-none
What is the difference between ”is None” and ”== None”, 2022 · 2022
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URL https://en.wikipedia.org/wiki/Precision_and_recall#Imbalanced_data
Wikipedia - Precision and Recall for Imbalanced Data, 2022 · 2022
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Neural transfer learning for repairing security vulnerabilities in c code
Chen, Z., Kommrusch, S. J., and Monperrus, M · 2022
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Deep multi-task learning with low level tasks supervised at lower layers
Søgaard, A. and Goldberg, Y · 2038
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