  During software maintenance, programmers spend a lot of time on code
comprehension. Reading comments is an effective way for programmers to reduce
the reading and navigating time when comprehending source code. Therefore, as a
critical task in software engineering, code summarization aims to generate
brief natural language descriptions for source code. In this paper, we propose
a new code summarization model named CodeSum. CodeSum exploits the
attention-based sequence-to-sequence (Seq2Seq) neural network with
Structure-based Traversal (SBT) of Abstract Syntax Trees (AST). The AST
sequences generated by SBT can better present the structure of ASTs and keep
unambiguous. We conduct experiments on three large-scale corpora in different
program languages, i.e., Java, C#, and SQL, in which Java corpus is our new
proposed industry code extracted from Github. Experimental results show that
our method CodeSum outperforms the state-of-the-art significantly.
