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28th International Conference on Neural Information Processing

作者： Gao, Xuejian Jiang, Xue Wu, Qiong Wang, Xiao Lyu, Chen Lyu, Lei Shandong Normal Univ Sch Informat Sci & Engn Jinan Peoples R China

ISBN: (纸本)9783030923068;9783030923075

Automatically obtaining descriptions of the functions of code snippets in natural language, i.e., code summarization, is an important issue in software engineering. In recent years, abstract syntax tree (AST)-based code summarization models for modelling syntactic structures have continued to emerge. In addition to syntactics, the semantics of the code are gradually gaining attention. In this paper, we propose a code summarization approach that incorporates local-ADG and AST, called GTsum. In particular, we introduce a novel local-ADG-based approach that can effectively filter out irrelevant semantics in the modelling of the semantic structure of code. GTsum learns semantics and syntactics in the local-ADG and AST through graph convolutional networks (GCNs) and then fuses them using Transformer. We evaluate our model on two Java language datasets with several metrics. The results demonstrate that our model achieves state-of-the-art performance compared to the existing models.

关键词： Code summarization local api dependency graph Abstract syntax tree graph convolutional network

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GT-SimNet: Improving code automatic summarization via multi-modal similarity networks

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JOURNAL OF SYSTEMS AND SOFTWARE 2022年 194卷

作者： Gao, Xuejian Jiang, Xue Wu, Qiong Wang, Xiao Lyu, Chen Lyu, Lei Shandong Normal Univ Sch Informat Sci & Engn Shandong Prov Key Lab Distributed Comp Software No Jinan 250014 Peoples R China

Code summarization aims to generate high-quality functional summaries of code snippets to improve the efficiency of program development and maintenance. It is a pressing challenge for code summariza-tion models to capture more comprehensive code knowledge by integrating the feature correlations between the semantics and syntax of the code. In this paper, we propose a multi-modal similarity network based code summarization method: GT-SimNet. It proposes a novel code semantic modelling method based on a local application programming interface (api) dependency graph (local-ADG), which exhibits an excellent ability to mask irrelevant semantics outside the current code snippet. For code feature fusion, GT-SimNet uses the SimNet network to calculate the correlation coefficients between local-ADG and abstract syntax tree (AST) nodes and performs fusion under the influence of the correlation coefficients. Finally, it completes the prediction of the target summary by the generator. We conduct extensive experiments to evaluate the performance of GT-SimNet on two language datasets (Java and Solidity). The results show that GT-SimNet achieved BLEU scores of 38.73% and 41.36% on the two datasets, 1.47%similar to 2.68% higher than the best existing baseline. Importantly, GT-SimNet reduces the BLEU scores by 7.28% after removing local-ADG. This indicates that local-ADG is effective for the semantic representation of the code.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Code summarization local api dependency graph AST GCN

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