1. Ahmad, W.U., Chakraborty, S., Ray, B., Chang, K., 2020. A transformer-based approach for source code summarization. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5–10, 2020. pp. 4998–5007.
2. Allamanis, M., Peng, H., Sutton, C., 2016. A convolutional attention network for extreme summarization of source code. In: Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19–24, 2016. pp. 2091–2100.
3. Binkley, D.W., 2007. Source code analysis: A road map. In: International Conference on Software Engineering, ISCE 2007, Workshop on the Future of Software Engineering, FOSE 2007, May 23–25, 2007, Minneapolis, MN, USA. pp. 104–119.
4. Cho, K., van Merrienboer, B., Gülçehre, Ç., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y., 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25–29, 2014, Doha, Qatar, a Meeting of SIGDAT, a Special Interest Group of the ACL. pp. 1724–1734.
5. Empirical evaluation of gated recurrent neural networks on sequence modeling;Chung,2014