The papers to read for this session are [1] and [2]


[1] M. Allamanis, M. Brockschmidt, and M. Khademi, “Learning to represent programs with graphs,” arXiv preprint arXiv:1711.00740, 2017.

[2] P. Yin, G. Neubig, M. Allamanis, M. Brockschmidt, and A. L. Gaunt, “Learning to Represent Edits,” ArXiv e-prints, Oct. 2018.

[3] H. Z. Jian Zhang Xu Wang and X. Liu, “A novel neural source code representation based on abstract syntax tree,” in ICSE 2019, 2019.

[4] M. Tufano, C. Watson, G. Bavota, M. Di Penta, M. White, and D. Poshyvanyk, “Deep learning similarities from different representations of source code,” in 2018 ieee/acm 15th international conference on mining software repositories (msr), 2018, pp. 542–553.