The papers to read for this session are Allam17? and [1]

References

[1]
P. Yin, G. Neubig, M. Allamanis, M. Brockschmidt, and A. L. Gaunt, Learning to Represent Edits,” ArXiv e-prints, Oct. 2018.
[2]
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.
[3]
M. Allamanis, M. Brockschmidt, and M. Khademi, “Learning to represent programs with graphs,” in International conference on learning representations, 2018.
[4]
H. Z. Jian Zhang Xu Wang and X. Liu, “A novel neural source code representation based on abstract syntax tree,” in ICSE 2019, 2019.