151 citations · 400 across the 24 of their papers we have counts for
18 papers · 1 filter
Impact-driven Context Filtering For Cross-file Code Completion
Yanzhou Li, Shangqing Liu, Kangjie Chen +2
Retrieval-augmented generation (RAG) has recently demonstrated considerable potential for repository-level code completion, as it integrates cross-file knowledge with in-file prece…
From Context to Intent: Reasoning-Guided Function-Level Code Completion
Yanzhou Li, Tianlin Li, Yiran Zhang +4
The growing capabilities of Large Language Models (LLMs) have led to their widespread adoption for function completion within code repositories. Recent studies on such tasks show p…
SpecEval: Evaluating Code Comprehension in Large Language Models via Program Specifications
Lezhi Ma, Shangqing Liu, Lei Bu +3
Large Language models have achieved impressive performance in automated software engineering. Extensive efforts have been made to evaluate the abilities of code LLMs in various asp…
Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications
Wei Ma, Daoyuan Wu, Yuqiang Sun +5
Smart contracts are decentralized applications built atop blockchains like Ethereum. Recent research has shown that large language models (LLMs) have potential in auditing smart co…
SpecGen: Automated Generation of Formal Program Specifications via Large Language Models
Lezhi Ma, Shangqing Liu, Yi Li +2
Formal program specifications play a crucial role in various stages of software development. However, manually crafting formal program specifications is rather difficult, making th…
Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases
Ze Tang, Jidong Ge, Shangqing Liu +4
Large Language Models (LLMs) have demonstrated remarkable performance in code completion. However, due to the lack of domain-specific knowledge, they may not be optimal in completi…
Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Study
Qi Guo, Junming Cao, Xiaofei Xie +4
Code review is an essential activity for ensuring the quality and maintainability of software projects. However, it is a time-consuming and often error-prone task that can signific…
Exploring Code Analysis: Zero-Shot Insights on Syntax and Semantics with LLMs
Wei Ma, Zhihao Lin, Shangqing Liu +8
Code analysis is fundamental in Software Engineering, supporting debugging, optimization, and security assessment. Human developers approach it through syntax parsing, static seman…
Evaluating AIGC Detectors on Code Content
Jian Wang, Shangqing Liu, Xiaofei Xie +1
Artificial Intelligence Generated Content (AIGC) has garnered considerable attention for its impressive performance, with ChatGPT emerging as a leading AIGC model that produces hig…
ContraBERT: Enhancing Code Pre-trained Models via Contrastive Learning
Shangqing Liu, Bozhi Wu, Xiaofei Xie +2
Large-scale pre-trained models such as CodeBERT, GraphCodeBERT have earned widespread attention from both academia and industry. Attributed to the superior ability in code represen…
Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities
Wei Ma, Shangqing Liu, Mengjie Zhao +5
Past research has examined how well these models grasp code syntax, yet their understanding of code semantics still needs to be explored. We extensively analyze seven code models t…
TransRepair: Context-aware Program Repair for Compilation Errors
Xueyang Li, Shangqing Liu, Ruitao Feng +4
Automatically fixing compilation errors can greatly raise the productivity of software development, by guiding the novice or AI programmers to write and debug code. Recently, learn…
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…
CommitBART: A Large Pre-trained Model for GitHub Commits
Shangqing Liu, Yanzhou Li, Xiaofei Xie +1
GitHub commits, which record the code changes with natural language messages for description, play a critical role for software developers to comprehend the software evolution. To…
Learning Program Semantics with Code Representations: An Empirical Study
Jing Kai Siow, Shangqing Liu, Xiaofei Xie +2
Program semantics learning is the core and fundamental for various code intelligent tasks e.g., vulnerability detection, clone detection. A considerable amount of existing works pr…
GraphSearchNet: Enhancing GNNs via Capturing Global Dependencies for Semantic Code Search
Shangqing Liu, Xiaofei Xie, Jingkai Siow +3
Code search aims to retrieve accurate code snippets based on a natural language query to improve software productivity and quality. With the massive amount of available programs su…
ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Ranking
Shangqing Liu, Cuiyun Gao, Sen Chen +2
Commit messages record code changes (e.g., feature modifications and bug repairs) in natural language, and are useful for program comprehension. Due to the frequent updates of soft…
Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks
Yaqin Zhou, Shangqing Liu, Jingkai Siow +2
Vulnerability identification is crucial to protect the software systems from attacks for cyber security. It is especially important to localize the vulnerable functions among the s…