Augmenting Java method comments generation with context information based on neural networks

Zhou, Yu; Yan, Xin; Yang, Wenhua; Chen, Taolue; Huang, Zhiqiu

doi:10.1016/j.jss.2019.07.087

Cited by 31 publications

(10 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LeClair et al [21] aimed to combine words from code with code structure from AST. Furthermore, techniques have been put forward to enhance the performance, e.g., approaches based on reinforcement learning [41] or aided with contextual information [52]. [38].…”

Section: Threats To Validitymentioning

confidence: 99%

“…Code comment generation aims to generate readable natural language descriptions of source code snippets, which plays an important role in facilitating program comprehension. Encouraged by the great success of deep learning methods in typical application areas such as computer vision and natural language processing, researchers have proposed deep neural network (DNN) based approaches for the code comment generation task [1,14,52], aiming to improve the quality of the generated comments.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adversarial Robustness of Deep Code Comment Generation

Zhou

Zhang

Shen

et al. 2022

ACM Trans. Softw. Eng. Methodol.

Self Cite

View full text Add to dashboard Cite

Deep neural networks (DNNs) have shown remarkable performance in a variety of domains such as computer vision, speech recognition, and natural language processing. Recently they also have been applied to various software engineering tasks, typically involving processing source code. DNNs are well-known to be vulnerable to adversarial examples, i.e., fabricated inputs that could lead to various misbehaviors of the DNN model while being perceived as benign by humans. In this paper, we focus on the code comment generation task in software engineering and study the robustness issue of the DNNs when they are applied to this task. We propose ACCENT ( A dversarial C ode C omment g EN era T or), an identifier substitution approach to craft adversarial code snippets, which are syntactically correct and semantically close to the original code snippet, but may mislead the DNNs to produce completely irrelevant code comments. In order to improve the robustness, ACCENT also incorporates a novel training method, which can be applied to existing code comment generation models. We conduct comprehensive experiments to evaluate our approach by attacking the mainstream encoder-decoder architectures on two large-scale publicly available datasets. The results show that ACCENT efficiently produces stable attacks with functionality-preserving adversarial examples, and the generated examples have better transferability compared with the baselines. We also confirm, via experiments, the effectiveness in improving model robustness with our training method.

show abstract

Section: Threats To Validitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adversarial Robustness of Deep Code Comment Generation

Zhou

Zhang

Shen

et al. 2022

ACM Trans. Softw. Eng. Methodol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Liu et al [45] utilized the knowledge of the call dependency between the source code and the dependency of codes. Zhou et al [46] proposed a method ContextCC, which uses the program analysis to extract context information (i.e., the methods and their dependency). Haque et al [47] modeled the file context (i.e., other methods in the same file) of methods, then they used an attention mechanism to find words and concepts to generate comments.…”

Section: Related Workmentioning

confidence: 99%

ComFormer: Code Comment Generation via Transformer and Fusion Method-based Hybrid Code Representation

Yang¹,

Chen²,

Cao³

et al. 2021

Preprint

View full text Add to dashboard Cite

Developers often write low-quality code comments due to the lack of programming experience, which can reduce the efficiency of developers' program comprehension. Therefore, developers hope that code comment generation tools can be developed to illustrate the functionality and purpose of the code. Recently, researchers mainly model this problem as the neural machine translation problem and tend to use deep learning-based methods. In this study, we propose a novel method ComFormer based on Transformer and fusion method-based hybrid code presentation. Moreover, to alleviate OOV (out-of-vocabulary) problem and speed up model training, we further utilize the Byte-BPE algorithm to split identifiers and Sim SBT method to perform AST Traversal. We compare ComFormer with seven state-of-the-art baselines from code comment generation and neural machine translation domains. Comparison results show the competitiveness of ComFormer in terms of three performance measures. Moreover, we perform a human study to verify that ComFormer can generate high-quality comments.

show abstract

“…LeClair et al [21] aimed to combine words from code with code structure from AST. Furthermore, techniques have been put forward to enhance the performance, e.g., approaches based on reinforcement learning [40] or aided with contextual information [51]. [37].…”

Section: Related Workmentioning

confidence: 99%

“…Code comment generation aims to generate readable natural language descriptions of source code snippets, which plays an important role in facilitating program comprehension. Encouraged by the great success of deep learning methods in typical application areas such as computer vision and natural language processing, researchers have proposed deep neural network (DNN) based approaches for the code comment generation task [1,14,51], aiming to improve the quality of the generated comments.…”

Section: Introductionmentioning

confidence: 99%

Adversarial Robustness of Deep Code Comment Generation

Zhou¹,

Zhang²,

Shen³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Deep neural networks (DNNs) have shown remarkable performance in a variety of domains such as computer vision, speech recognition, or natural language processing. Recently they also have been applied to various software engineering tasks, typically involving processing source code. DNNs are well-known to be vulnerable to adversarial examples, i.e., fabricated inputs that could lead to various misbehaviors of the DNN model while being perceived as benign by humans. In this paper, we focus on the code comment generation task in software engineering and study the robustness issue of the DNNs when they are applied to this task. We propose ACCENT, an identifier substitution approach to craft adversarial code snippets, which are syntactically correct and functionality-preserving with respect to the original code snippet, but may mislead the DNNs to produce completely irrelevant code comments. In order to improve the robustness, ACCENT also incorporates a novel training method, which can be applied to existing code comment generation models. We conduct comprehensive experiments to evaluate our approach by attacking the mainstream encoder-decoder architectures on two large-scale publicly available datasets. The results show that ACCENT efficiently produces stable attacks with functionality-preserving adversarial examples, and the generated examples have better transferability compared with baselines. We also confirm, via experiments, the effectiveness in improving model robustness with our training method.

show abstract

Augmenting Java method comments generation with context information based on neural networks

Cited by 31 publications

References 5 publications

Adversarial Robustness of Deep Code Comment Generation

Adversarial Robustness of Deep Code Comment Generation

ComFormer: Code Comment Generation via Transformer and Fusion Method-based Hybrid Code Representation

Adversarial Robustness of Deep Code Comment Generation

Contact Info

Product

Resources

About