[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.