Retrieve and Refine: Exemplar-based Neural Comment Generation

• URL: http://arxiv.org/abs/2010.04459v1
• Date: Fri, 9 Oct 2020 09:33:10 GMT
• Title: Retrieve and Refine: Exemplar-based Neural Comment Generation
• Authors: Bolin Wei, Yongmin Li, Ge Li, Xin Xia, Zhi Jin
• Abstract summary: Comments of similar code snippets are helpful for comment generation. We design a novel seq2seq neural network that takes the given code, its AST, its similar code, and its exemplar as input. We evaluate our approach on a large-scale Java corpus, which contains about 2M samples.
• Score: 27.90756259321855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Code comment generation which aims to automatically generate natural language descriptions for source code, is a crucial task in the field of automatic software development. Traditional comment generation methods use manually-crafted templates or information retrieval (IR) techniques to generate summaries for source code. In recent years, neural network-based methods which leveraged acclaimed encoder-decoder deep learning framework to learn comment generation patterns from a large-scale parallel code corpus, have achieved impressive results. However, these emerging methods only take code-related information as input. Software reuse is common in the process of software development, meaning that comments of similar code snippets are helpful for comment generation. Inspired by the IR-based and template-based approaches, in this paper, we propose a neural comment generation approach where we use the existing comments of similar code snippets as exemplars to guide comment generation. Specifically, given a piece of code, we first use an IR technique to retrieve a similar code snippet and treat its comment as an exemplar. Then we design a novel seq2seq neural network that takes the given code, its AST, its similar code, and its exemplar as input, and leverages the information from the exemplar to assist in the target comment generation based on the semantic similarity between the source code and the similar code. We evaluate our approach on a large-scale Java corpus, which contains about 2M samples, and experimental results demonstrate that our model outperforms the state-of-the-art methods by a substantial margin.

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