2017/08/06 by Xing Hu, Yuhan Wei, Hu, Xing +5 · 1 citation
Computer Science · Engineering · #FOS: Computer and information sciences #Robotics and Automated Systems #Software Engineering (cs.SE) #Software Engineering Research #Software Testing and Debugging Techniques
paper · pdf · doi:10.48550/arxiv.1708.01837
openalex publication_date 2017/08/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.