4 citations · 9 across the 13 of their papers we have counts for
7 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…
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…
Pros and Cons! Evaluating ChatGPT on Software Vulnerability
Xin Yin
This paper proposes a pipeline for quantitatively evaluating interactive LLMs such as ChatGPT using publicly available dataset. We carry out an extensive technical evaluation of Ch…
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…