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20212025
most citedPrecise Learning of Source Code Contextual Semantics via Hierarchical Dependence Structure and Graph Attention Networks

36 citations · 253 across the 30 of their papers we have counts for

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Showing 2022 · cs.SEShow all

10 papers · 2 filters

cs.SE2022★ 4 cited

CodePAD: Sequence-based Code Generation with Pushdown Automaton

Yihong Dong, Xue Jiang, Yuchen Liu +2

In the process of code generation, it is essential to guarantee the generated code satisfies grammar constraints of programming language (PL). However, neglecting grammar constrain…

cs.SE2022★ 15 cited

Poison Attack and Defense on Deep Source Code Processing Models

Jia Li, Zhuo Li, Huangzhao Zhang +4

In the software engineering community, deep learning (DL) has recently been applied to many source code processing tasks. Due to the poor interpretability of DL models, their secur…

cs.SE2022★ 29 cited

CodeEditor: Learning to Edit Source Code with Pre-trained Models

Jia Li, Ge Li, Zhuo Li +4

Developers often perform repetitive code editing activities for various reasons (e.g., code refactoring) during software development. Pre-trained code editing models have achieved…

cs.SE2022★ 2 cited

Incorporating Domain Knowledge through Task Augmentation for Front-End JavaScript Code Generation

Sijie Shen, Xiang Zhu, Yihong Dong +3

Code generation aims to generate a code snippet automatically from natural language descriptions. Generally, the mainstream code generation methods rely on a large amount of paired…

cs.SE2022

Antecedent Predictions Are More Important Than You Think: An Effective Method for Tree-Based Code Generation

Yihong Dong, Ge Li, Xue Jiang +1

Code generation focuses on the automatic conversion of natural language (NL) utterances into code snippets. The sequence-to-tree (Seq2Tree) approaches are proposed for code generat…

cs.SE2022★ 6 cited

Learning Program Representations with a Tree-Structured Transformer

Wenhan Wang, Kechi Zhang, Ge Li +4

Learning vector representations for programs is a critical step in applying deep learning techniques for program understanding tasks. Various neural network models are proposed to…