26 citations · 52 across the 22 of their papers we have counts for
10 papers · 1 filter
What You See Is What You Get: Attention-based Self-guided Automatic Unit Test Generation
Xin Yin, Chao Ni, Xiaodan Xu +1
Software defects heavily affect software's functionalities and may cause huge losses. Recently, many AI-based approaches have been proposed to detect defects, which can be divided…
Distinguishing LLM-generated from Human-written Code by Contrastive Learning
Xiaodan Xu, Chao Ni, Xinrong Guo +4
Large language models (LLMs), such as ChatGPT released by OpenAI, have attracted significant attention from both industry and academia due to their demonstrated ability to generate…
Learning-based Models for Vulnerability Detection: An Extensive Study
Chao Ni, Liyu Shen, Xiaodan Xu +2
Though many deep learning-based models have made great progress in vulnerability detection, we have no good understanding of these models, which limits the further advancement of m…
Improving the Ability of Pre-trained Language Model by Imparting Large Language Model's Experience
Xin Yin, Chao Ni, Xiaodan Xu +2
Large Language Models (LLMs) and pre-trained Language Models (LMs) have achieved impressive success on many software engineering tasks (e.g., code completion and code generation).…
ThinkRepair: Self-Directed Automated Program Repair
Xin Yin, Chao Ni, Shaohua Wang +3
Though many approaches have been proposed for Automated Program Repair (APR) and indeed achieved remarkable performance, they still have limitations in fixing bugs that require ana…
Rectifier: Code Translation with Corrector via LLMs
Xin Yin, Chao Ni, Tien N. Nguyen +2
Software migration is garnering increasing attention with the evolution of software and society. Early studies mainly relied on handcrafted translation rules to translate between t…
CasModaTest: A Cascaded and Model-agnostic Self-directed Framework for Unit Test Generation
Chao Ni, Xiaoya Wang, Liushan Chen +4
Though many machine learning (ML)-based unit testing generation approaches have been proposed and indeed achieved remarkable performance, they still have several limitations in eff…
MegaVul: A C/C++ Vulnerability Dataset with Comprehensive Code Representation
Chao Ni, Liyu Shen, Xiaohu Yang +2
We constructed a newly large-scale and comprehensive C/C++ vulnerability dataset named MegaVul by crawling the Common Vulnerabilities and Exposures (CVE) database and CVE-related o…
Natural Is The Best: Model-Agnostic Code Simplification for Pre-trained Large Language Models
Yan Wang, Xiaoning Li, Tien Nguyen +3
Pre-trained Large Language Models (LLM) have achieved remarkable successes in several domains. However, code-oriented LLMs are heavy in computational complexity, and quadratically…
Multitask-based Evaluation of Open-Source LLM on Software Vulnerability
Xin Yin, Chao Ni, Shaohua Wang
This paper proposes a pipeline for quantitatively evaluating interactive Large Language Models (LLMs) using publicly available datasets. We carry out an extensive technical evaluat…