36 citations · 253 across the 30 of their papers we have counts for
18 papers · 1 filter
ZC3: Zero-Shot Cross-Language Code Clone Detection
Jia Li, Chongyang Tao, Zhi Jin +2
Developers introduce code clones to improve programming productivity. Many existing studies have achieved impressive performance in monolingual code clone detection. However, durin…
Hot or Cold? Adaptive Temperature Sampling for Code Generation with Large Language Models
Yuqi Zhu, Jia Li, Ge Li +3
Recently, Large Language Models (LLMs) have shown impressive abilities in code generation. However, existing LLMs' decoding strategies are designed for Natural Language (NL) genera…
STEAM: Simulating the InTeractive BEhavior of ProgrAMmers for Automatic Bug Fixing
Yuwei Zhang, Zhi Jin, Ying Xing +1
Bug fixing holds significant importance in software development and maintenance. Recent research has made notable progress in exploring the potential of large language models (LLMs…
PACE: Improving Prompt with Actor-Critic Editing for Large Language Model
Yihong Dong, Kangcheng Luo, Xue Jiang +2
Large language models (LLMs) have showcased remarkable potential across various tasks by conditioning on prompts. However, the quality of different human-written prompts leads to s…
Automated Static Warning Identification via Path-based Semantic Representation
Yuwei Zhang, Ying Xing, Ge Li +1
Despite their ability to aid developers in detecting potential defects early in the software development life cycle, static analysis tools often suffer from precision issues (i.e.,…
WELL: Applying Bug Detectors to Bug Localization via Weakly Supervised Learning
Zhuo Li, Huangzhao Zhang, Zhi Jin +1
Bug localization, which is used to help programmers identify the location of bugs in source code, is an essential task in software development. Researchers have already made effort…
SAGA: Summarization-Guided Assert Statement Generation
Yuwei Zhang, Zhi Jin, Zejun Wang +2
Generating meaningful assert statements is one of the key challenges in automated test case generation, which requires understanding the intended functionality of the tested code.…
Neural Program Repair with Program Dependence Analysis and Effective Filter Mechanism
Yuwei Zhang, Ge Li, Zhi Jin +1
Automated program repair is a crucial task for improving the efficiency of software developers. Recently, neural-based techniques have demonstrated significant promise in generatin…
Structured Chain-of-Thought Prompting for Code Generation
Jia Li, Ge Li, Yongmin Li +1
Large Language Models (LLMs) (e.g., ChatGPT) have shown impressive performance in code generation. LLMs take prompts as inputs, and Chain-of-Thought (CoT) prompting is the state-of…
Self-Edit: Fault-Aware Code Editor for Code Generation
Kechi Zhang, Zhuo Li, Jia Li +2
Large language models (LLMs) have demonstrated an impressive ability to generate codes on competitive programming tasks. However, with limited sample numbers, LLMs still suffer fro…
ToolCoder: Teach Code Generation Models to use API search tools
Kechi Zhang, Huangzhao Zhang, Ge Li +3
Automatically generating source code from natural language descriptions has been a growing field of research in recent years. However, current large-scale code generation models of…
Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning
Mingyang Geng, Shangwen Wang, Dezun Dong +5
Code comment generation aims at generating natural language descriptions for a code snippet to facilitate developers' program comprehension activities. Despite being studied for a…
Self-collaboration Code Generation via ChatGPT
Yihong Dong, Xue Jiang, Zhi Jin +1
Although Large Language Models (LLMs) have demonstrated remarkable code-generation ability, they still struggle with complex tasks. In real-world software development, humans usual…
Implant Global and Local Hierarchy Information to Sequence based Code Representation Models
Kechi Zhang, Zhuo Li, Zhi Jin +1
Source code representation with deep learning techniques is an important research field. There have been many studies that learn sequential or structural information for code repre…
AceCoder: Utilizing Existing Code to Enhance Code Generation
Jia Li, Yunfei Zhao, Yongmin Li +2
Large Language Models (LLMs) have shown great success in code generation. LLMs take as the input a prompt and output the code. A key question is how to make prompts (i.e., Promptin…
Self-planning Code Generation with Large Language Models
Xue Jiang, Yihong Dong, Lecheng Wang +5
Although large language models (LLMs) have demonstrated impressive ability in code generation, they are still struggling to address the complicated intent provided by humans. It is…
SkCoder: A Sketch-based Approach for Automatic Code Generation
Jia Li, Yongmin Li, Ge Li +3
Recently, deep learning techniques have shown great success in automatic code generation. Inspired by the code reuse, some researchers propose copy-based approaches that can copy t…
CodeScore: Evaluating Code Generation by Learning Code Execution
Yihong Dong, Jiazheng Ding, Xue Jiang +3
A proper code evaluation metric (CEM) profoundly impacts the evolution of code generation, which is an important research field in NLP and software engineering. Prevailing match-ba…