SeCNN: A semantic CNN parser for code comment generation

Abstract

A code comment generation system can summarize the semantic information of source code and generate a natural language description, which can help developers comprehend programs and reduce time cost spent during software maintenance. Most of state-of-the-art approaches use RNN (Recurrent Neural Network)-based encoder–decoder neural networks. However, this kind of method may not generate high-quality description when summarizing the information among several code blocks that are far from each other (i.e., the long-dependency problem). In this paper, we propose a novel Semantic CNN parser SeCNN for code comment generation. In particular, we use a CNN (Convolutional Neural Network) to alleviate the long-dependency problem and design several novel components, including source code-based CNN and AST-based CNN, to capture the semantic information of the source code. The evaluation is conducted on a widely-used large-scale dataset of 87,136 Java methods. Experimental results show that SeCNN achieves better performance (i.e., 44.69% in terms of BLEU and 26.88% in terms of METEOR) and has lower execution time cost when compared with five state-of-the-art baselines.