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Programming Language Processing (PLP) using machine learning has made vast improvements in the past few years.
Allamanis, M., Sutton, C.: Mining Source Code Repositories at Massive Scale using Language Modeling. In: The 10th Working Conference on Mining Software Repositories. pp. 207–216. IEEE (2013)
2013
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Just, R., Jalali, D., Ernst, M.D.: Defects4j: A database of existing faults to enable controlled testing studies for java programs. In: Proceedings of the 2014 International Symposium on Software Testing and Analysis. p. 437–440. ISSTA 2014, Association for Computing Machinery, New York, NY, USA (2014)
2014
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Svajlenko, J., Islam, J.F., Keivanloo, I., Roy, C.K., Mia, M.M.: Towards a big data curated benchmark of inter-project code clones. In: 2014 IEEE International Conference on Software Maintenance and Evolution. pp. 476–480 (2014)
2014
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Nguyen, A.T., Nguyen, T.T., Nguyen, T.N.: Divide-and-conquer approach for multi-phase statistical migration for source code (t). In: 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE). pp. 585–596 (2015)
2015
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Mou, L., Li, G., Zhang, L., Wang, T., Jin, Z.: Convolutional neural networks over tree structures for programming language processing. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. pp. 1287–1293 (2016)
2016
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Raychev, V., Bielik, P., Vechev, M.: Probabilistic model for code with decision trees. SIGPLAN Not. 51
2016
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Cummins, C., Petoumenos, P., Wang, Z., Leather, H.: End-to-end deep learning of optimization heuristics. In: PACT. ACM (2017)
2017
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Liao, C., Lin, P.H., Asplund, J., Schordan, M., Karlin, I.: Dataracebench: a benchmark suite for systematic evaluation of data race detection tools. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. pp. 1–14 (2017)
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need (2017)
2017
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
2017
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Allamanis, M., Barr, E.T., Devanbu, P., Sutton, C.: A survey of machine learning for big code and naturalness. ACM Computing Surveys (CSUR) 51
2018
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Ashouri, A.H., Killian, W., Cavazos, J., Palermo, G., Silvano, C.: A survey on compiler autotuning using machine learning. ACM Comput. Surv. 51
2018
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Chen, X., Liu, C., Song, D.: Tree-to-tree neural networks for program translation. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. p. 2552–2562. NIPS’18, Curran Associates Inc., Red Hook, NY, USA (2018)
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2018
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Hellendoorn, V.J., Bird, C., Barr, E.T., Allamanis, M.: Deep learning type inference. In: Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. p. 152–162. ESEC/FSE 2018, Association for Computing Machinery, New York, NY, USA (2018)
2018
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Iyer, S., Konstas, I., Cheung, A., Zettlemoyer, L.: Mapping language to code in programmatic context (2018)
2018
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Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al.: Improving language understanding by generative pre-training (2018)
2018
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Yin, P., Deng, B., Chen, E., Vasilescu, B., Neubig, G.: Learning to mine aligned code and natural language pairs from stack overflow. In: International Conference on Mining Software Repositories. pp. 476–486. MSR, ACM (2018)
2018
Cited alongside, same era.
Hu, X., Li, G., Xia, X., Lo, D., Jin, Z.: Deep code comment generation with hybrid lexical and syntactical information - empirical software engineering (Jun 2019)
2019
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Husain, H., Wu, H.H., Gazit, T., Allamanis, M., Brockschmidt, M.: Codesearchnet challenge: Evaluating the state of semantic code search (2019)
2019
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Tufano, M., Watson, C., Bavota, G., Penta, M.D., White, M., Poshyvanyk, D.: An empirical study on learning bug-fixing patches in the wild via neural machine translation. ACM Trans. Softw. Eng. Methodol. 28
2019
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2021
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Cummins, C., Fisches, Z., Ben-Nun, T., Hoefler, T., O’Boyle, M., Leather, H.: ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations. In: Thirty-eighth International Conference on Machine Learning (ICML) (2021)
2021
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Huang, J., Tang, D., Shou, L., Gong, M., Xu, K., Jiang, D., Zhou, M., Duan, N.: Cosqa: 20,000+ web queries for code search and question answering (2021)
2021
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Jiang, X., Zheng, Z., Lyu, C., Li, L., Lyu, L.: Treebert: A tree-based pre-trained model for programming language. In: de Campos, C., Maathuis, M.H. (eds.) Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence. Proceedings of Machine Learning Research, vol. 161, pp. 54–63. PMLR (27–30 Jul 2021)
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Zhou, Y., Liu, S., Siow, J., Du, X., Liu, Y.: Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks (2019)
2019
Cited alongside, same era.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33
2020
Cited alongside, same era.
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers (2020)
2020
Cited alongside, same era.
Chami, I., Abu-El-Haija, S., Perozzi, B., Ré, C., Murphy, K.: Machine learning on graphs: A model and comprehensive taxonomy (2020)
2020
Cited alongside, same era.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale (2020)
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Guo, D., Ren, S., Lu, S., Feng, Z., Tang, D., Liu, S., Zhou, L., Duan, N., Svyatkovskiy, A., Fu, S., Tufano, M., Deng, S.K., Clement, C., Drain, D., Sundaresan, N., Yin, J., Jiang, D., Zhou, M.: Graphcodebert: Pre-training code representations with data flow (2020)
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2021
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Kalyan, K.S., Rajasekharan, A., Sangeetha, S.: Ammus : A survey of transformer-based pretrained models in natural language processing (2021)
2021
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Kim, Y.J., Awan, A.A., Muzio, A., Cruz Salinas, F., Lu, L., Hendy, A., Rajbhandari, S., He, Y., Hassan Awadalla, H.: Scalable and efficient moe training for multitask multilingual models (September 2021)
2021
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2021
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2021
Later among the works it cites.
2021
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Puri, R., Kung, D.S., Janssen, G., Zhang, W., Domeniconi, G., Zolotov, V., Dolby, J., Chen, J., Choudhury, M., Decker, L., Thost, V., Buratti, L., Pujar, S., Ramji, S., Finkler, U., Malaika, S., Reiss, F.: Codenet: A large-scale ai for code dataset for learning a diversity of coding tasks (2021)
2021
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Sarker, I.H.: Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Computer Science 2
2021
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Wang, X., Wang, Y., Mi, F., Zhou, P., Wan, Y., Liu, X., Li, L., Wu, H., Liu, J., Jiang, X.: Syncobert: Syntax-guided multi-modal contrastive pre-training for code representation (2021)
2021
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Wang, Y., Wang, W., Joty, S., Hoi, S.C.: CodeT5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. pp. 8696–8708. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic (Nov 2021). https://doi.org/10.18653/v1/2021.emnlp-main.685
2021
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2022
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Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Lago, A.D., Hubert, T., Choy, P., d’Autume, C.d.M., Babuschkin, I., Chen, X., Huang, P.S., Welbl, J., Gowal, S., Cherepanov, A., Molloy, J., Mankowitz, D.J., Robson, E.S., Kohli, P., de Freitas, N., Kavukcuoglu, K., Vinyals, O.: Competition-level code generation with alphacode (2022)
2022
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Wang, X., Wang, Y., Wan, Y., Wang, J., Zhou, P., Li, L., Wu, H., Liu, J.: Code-mvp: Learning to represent source code from multiple views with contrastive pre-training (2022)
2022
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Yu, J., Xu, Y., Koh, J.Y., Luong, T., Baid, G., Wang, Z., Vasudevan, V., Ku, A., Yang, Y., Ayan, B.K., Hutchinson, B., Han, W., Parekh, Z., Li, X., Zhang, H., Baldridge, J., Wu, Y.: Scaling autoregressive models for content-rich text-to-image generation (2022)
2022
Closest in time.