A Code Completion Approach Combining Pointer Network and Transformer-XL Network
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-54521-4_17
Reference27 articles.
1. Izadi, M., Gismondi, R., Gousios, G.: CodeFill: multi-token code completion by jointly learning from structure and naming sequences. In: Proceedings of the 44th International Conference on Software Engineering, pp. 401–412 (2022)
2. Yang, Y., Xiang, C.: Improve language modelling for code completion by tree language model with tree encoding of context (S), pp. 675–777 (2019)
3. Fang, L., Huang, Z., Zhou, Y., Chen., T.: Adaptive code completion with meta-learning. In: Proceedings of the 12th Asia-Pacific Symposium on Internetware, pp. 116–125 (2020)
4. Popov, A., Orekhov, D., Litvinov, D.: Time-efficient code completion model for the R programming language. In: Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021), pp. 34–39 (2021)
5. Kyaw, H.H.S., Funabiki, N., Kuribayashi, M.: An implementation of offline answering function for code completion problem in PLAS. In: 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech), pp. 162–165 (2021)
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