SeCNN: A semantic CNN parser for code comment generation

Li, Zheng; Wu, Yonghao; Peng, Bin; Chen, Xiang; Sun, Zeyu; Liu, Yong; Yu, Deli

doi:10.1016/j.jss.2021.111036

Cited by 34 publications

(8 citation statements)

References 19 publications

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“…Haiduc et al [18] used only lexical information using code tokens for comment generation. Hybrid-DeepCom [11], ComFormer [7], SeCNN [24], and SeTransformer [4] use the source code structure information as well as the source code in comment generation. These methods use lexical and syntactic information together.…”

Section: A Source Code Information 1) Code Sequencementioning

confidence: 99%

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ALSI-Transformer: Transformer-Based Code Comment Generation With Aligned Lexical and Syntactic Information

Park

Kim

2023

IEEE Access

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Code comments explain the operational process of a computer program and increase the long-term productivity of programming tasks such as debugging and maintenance. Therefore, developing methods that automatically generate natural language comments from programming code is required. With the development of deep learning, various excellent models in the natural language processing domain have been applied for comment generation tasks, and recent studies have improved performance by simultaneously using the lexical information of the code token and the syntactical information obtained from the syntax tree. In this paper, to improve the accuracy of automatic comment generation, we introduce a novel syntactic sequence, Code-Aligned Type sequence (CAT), to align the order and length of lexical and syntactic information, and we propose a new neural network model, Aligned Lexical and Syntactic information-Transformer (ALSI-Transformer), based on a transformer that encodes the aligned multimodal information with convolution and embedding aggregation layers. Through in-depth experiments, we compared ALSI-Transformer with current baseline methods using standard machine translation metrics and demonstrate that the proposed method achieves state-of-the-art performance in code comment generation.

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Section: A Source Code Information 1) Code Sequencementioning

confidence: 99%

“…We use three metrics, BLEU and METEOR, which are popular performance metrics in NMT. These measures have also been widely used in previous code comment generation studies [4], [6], [7], [11], [24], [34].…”

Section: Construct Threatsmentioning

confidence: 99%

ALSI-Transformer: Transformer-Based Code Comment Generation With Aligned Lexical and Syntactic Information

Park

Kim

2023

IEEE Access

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“…Stapleton et al [71] showed that the performance measures (such as BLEU, ROUGE, and METEOR), which were commonly used for the code summarization task, cannot necessarily capture how machinegenerated code summaries actually affect program comprehension of developers. Therefore, we conduct the human study by following the methodology used in previous studies [72], [73]. Since the Dual Model can achieve the best performance among all the baselines, we focus on analyzing the differences between comments generated by DualSC and the baseline Dual Model.…”

Section: B Human Study On the Shellcode Summarization Taskmentioning

confidence: 99%

DualSC: Automatic Generation and Summarization of Shellcode via Transformer and Dual Learning

Yang¹,

Chen²,

Zhou³

et al. 2022

Preprint

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“…SeCNN is proposed by Li et al [26], and uses two layer CNN to encode semantic information of the source code. One CNN is used to extract lexical information from the code tokens, and another CNN is used to extract syntactic information from the AST.…”

Section: Baselinesmentioning

confidence: 99%

MMF3: Neural Code Summarization Based on Multi-Modal Fine-Grained Feature Fusion

Gao

Lyu

et al. 2022

Proceedings of the 16th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement

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Background: Code summarization automatically generates the corresponding natural language descriptions according to the input code to characterize the function implemented by source code. Comprehensiveness of code representation is critical to code summarization task. However, most existing approaches typically use coarse-grained fusion methods to integrate multi-modal features. They generally represent different modalities of a piece of code, such as an Abstract Syntax Tree (AST) and a token sequence, as two embeddings and then fuse the two ones at the AST/code levels. Such a coarse integration makes it difficult to learn the correlations between fine-grained code elements across modalities effectively. Aims: This study intends to improve the model's prediction performance for high-quality code summarization by accurately aligning and fully fusing semantic and syntactic structure information of source code at node/token levels. Method: This paper proposes a Multi-Modal Fine-grained Feature Fusion approach (MMF3) for neural code summarization. The method uses the Transformer architecture. In particular, we introduce a novel fine-grained fusion method, which allows fine-grained fusion of multiple code modalities at the token and node levels. Specifically, we use this method to fuse information from both token and AST modalities and apply the fused features to code summarization. Results: We conduct experiments on one Java and one Python datasets, and evaluate generated summaries using four metrics. The results show that: 1) the performance of our model outperforms the current stateof-the-art models, and 2) the ablation experiments show that our proposed fine-grained fusion method can effectively improve the accuracy of generated summaries. Conclusion: MMF3 can mine the relationships between cross-modal elements and perform accurate fine-grained element-level alignment fusion accordingly.

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SeCNN: A semantic CNN parser for code comment generation

Cited by 34 publications

References 19 publications

ALSI-Transformer: Transformer-Based Code Comment Generation With Aligned Lexical and Syntactic Information

ALSI-Transformer: Transformer-Based Code Comment Generation With Aligned Lexical and Syntactic Information

DualSC: Automatic Generation and Summarization of Shellcode via Transformer and Dual Learning

MMF3: Neural Code Summarization Based on Multi-Modal Fine-Grained Feature Fusion

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