Papers to be discussed in this session are: [1], [2].

Bibliography

[1]
V. J. Hellendoorn, S. Proksch, H. C. Gall, and A. Bacchelli, “When code completion fails: A case study on real-world completions,” in Proceedings of the 41st international conference on software engineering, 2019, pp. 960–970.
[2]
J. Li, Y. Wang, M. R. Lyu, and I. King, “Code completion with neural attention and pointer networks,” arXiv preprint arXiv:1711.09573, 2017.
[4]
S. Luan, D. Yang, K. Sen, and S. Chandra, “Aroma: Code recommendation via structural code search,” arXiv preprint arXiv:1812.01158, 2018.
[4]
S. Luan, D. Yang, K. Sen, and S. Chandra, “Aroma: Code recommendation via structural code search,” arXiv preprint arXiv:1812.01158, 2018.
[5]
V. Raychev, M. Vechev, and E. Yahav, “Code completion with statistical language models,” in Acm sigplan notices, 2014, vol. 49, pp. 419–428.
[6]
M. Bruch, M. Monperrus, and M. Mezini, “Learning from examples to improve code completion systems,” in Proceedings of the the 7th joint meeting of the european software engineering conference and the ACM SIGSOFT symposium on the foundations of software engineering, 2009, pp. 213–222.
[7]
X. Jin and F. Servant, “The hidden cost of code completion: Understanding the impact of the recommendation-list length on its efficiency,” in 2018 IEEE/ACM 15th international conference on mining software repositories (MSR), 2018, pp. 70–73.