The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Convolutional Neural Networks over Tree Structures for Programming Language Processing
Open accessAbstract
Programming language processing (similar to natural language processing) is a hot research topic in the field of software engineering; it has also aroused growing interest in the artificial intelligence community. However, different from a natural language sentence, a program contains rich, explicit, and complicated structural information. Hence, traditional NLP models may be inappropriate for programs. In this paper, we propose a novel tree-based convolutional neural network (TBCNN) for programming language processing, in which a convolution kernel is designed over programs' abstract syntax trees to capture structural information. TBCNN is a generic architecture for programming language processing; our experiments show its effectiveness in two different program analysis tasks: classifying programs according to functionality, and detecting code snippets of certain patterns. TBCNN outperforms baseline methods, including several neural models for NLP.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Recurrent neural network based language model
Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-343 · Cited by 5,390
A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841
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CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Lu, S., Guo, D., Ren, S., Huang, J., Svyatkovskiy, A., Blanco, A., Clement, C., Drain, D., Jiang, D., Tang, D., et al. (2021) · arXiv · doi:10.48550/arxiv.2102.04664 · Cited by 536
Self-citationDeep learning similarities from different representations of source code
Tufano, M., Watson, C., Bavota, G., Di Penta, M., White, M., & Poshyvanyk, D. (2018) · Proceedings of the 15th International Conference on Mining Software Repositories · doi:10.1145/3196398.3196431 · Cited by 158
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research
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A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes
Loyola, P., Marrese-Taylor, E., & Matsuo, Y. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p17-2045 · Cited by 128
Bui, N. D. Q., Yu, Y., & Jiang, L. (2021) · Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3404835.3462840 · Cited by 103
A Survey of Machine Learning for Big Code and Naturalness
Allamanis, M., Barr, E. T., Devanbu, P., & Sutton, C. (2019) · ACM Computing Surveys · doi:10.1145/3212695 · Cited by 150
A general path-based representation for predicting program properties
Alon, U., Zilberstein, M., Levy, O., & Yahav, E. (2018) · Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation · doi:10.1145/3192366.3192412 · Cited by 79
Unlearnable examples: protecting open-source software from unauthorized neural code learning
Ji, Z., Ma, P., & Wang, S. (2022) · International Conferences on Software Engineering and Knowledge Engineering · doi:10.18293/seke2022-066 · Cited by 5
JEMMA: An extensible Java dataset for ML4Code applications
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Source Code Plagiarism Detection Using Siamese BLSTM Network and Embedding Models
Manahi, M., Sulaiman, S., & Bakar, N. S. A. A. (2022) · Lecture Notes in Electrical Engineering · doi:10.1007/978-981-16-8515-6_31 · Cited by 3
Metadata
- Authors
- Mou, L., Li, G., Zhang, L., Wang, T., & Jin, Z.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2016-02-21
- Volume
- 30
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2282866165
- Cited by
- 675
- References
- 6
- FWCI
- 42.74
- Type
- conference paper
- Language
- en
- Open access
- diamond
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