Introduction

Code summarization methods automatically generate descriptions from source code and document programs. High-level summaries provided along with code snippets help developers better comprehend source code. Moreover, these summaries are useful in other applications such as code search. Summarization is a non-trivial problem in natural language processing. Fortunately, source code is highly structured. Recently researchers have tried to exploit the rich structure of source code by using code-specific features such as abstract syntax trees and program control flows.

Reading material

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

Bibliography

[1]
P. Fernandes, M. Allamanis, and M. Brockschmidt, “Structured neural summarization,” arXiv preprint arXiv:1811.01824, 2018.
[2]
S. Iyer, I. Konstas, A. Cheung, and L. Zettlemoyer, “Summarizing source code using a neural attention model,” in Proceedings of the 54th annual meeting of the association for computational linguistics (volume 1: Long papers), 2016, pp. 2073–2083.
[3]
J. Fowkes, P. Chanthirasegaran, R. Ranca, M. Allamanis, M. Lapata, and C. Sutton, “Autofolding for source code summarization,” IEEE Trans. Softw. Eng., vol. 43, no. 12, pp. 1095–1109, Dec. 2017.
[4]
M. Allamanis, H. Peng, and C. Sutton, “A convolutional attention network for extreme summarization of source code,” in International conference on machine learning, 2016, pp. 2091–2100.
[5]
U. Alon, S. Brody, O. Levy, and E. Yahav, “code2seq: Generating sequences from structured representations of code,” arXiv preprint arXiv:1808.01400, 2018.
[6]
Y. Wan et al., “Improving automatic source code summarization via deep reinforcement learning,” in Proceedings of the 33rd ACM/IEEE international conference on automated software engineering, 2018, pp. 397–407.
[7]
S. Xu, S. Zhang, W. Wang, X. Cao, C. Guo, and J. Xu, “Method name suggestion with hierarchical attention networks,” in Proceedings of the 2019 ACM SIGPLAN workshop on partial evaluation and program manipulation, 2019, pp. 10–21.