Code comment generation based on graph neural network enhanced transformer model for code understanding in open-source software ecosystems

Author:

Kuang Li,Zhou Cong,Yang XiaoxianORCID

Funder

national key r&d program of china

national natural science foundation of china

fundamental research funds for central universities of the central south university

Publisher

Springer Science and Business Media LLC

Subject

Software

Reference43 articles.

1. Ahmad, W.U., Chakraborty, S., Ray, B., Chang, K.W.: A transformer-based approach for source code summarization. arXiv preprint arXiv:200500653 (2020)

2. Allamanis, M., Brockschmidt, M., Khademi, M.: Learning to represent programs with graphs. arXiv preprint arXiv:1711.00740 (2017)

3. Allamanis, M., Peng, H., Sutton, C.: A convolutional attention network for extreme summarization of source code. In: International conference on machine learning, PMLR, pp. 2091–2100 (2016)

4. Allamanis, M., Barr, E.T., Devanbu, P., Sutton, C.: A survey of machine learning for big code and naturalness. ACM Comput. Surv. (CSUR) 51(4), 1–37 (2018)

5. Alon, U., Brody, S., Levy, O., Yahav, E.: code2seq: Generating sequences from structured representations of code. arXiv preprint arXiv:1808.01400 (2018)

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