no code implementations • EMNLP 2021 • Haiwen Hong, Jingfeng Zhang, Yin Zhang, Yao Wan, Yulei Sui
Obviously, unchanged fix is not the correct fix because it is the same as the buggy code that needs to be fixed.
1 code implementation • 26 Apr 2024 • Yang Wu, Yao Wan, Hongyu Zhang, Yulei Sui, Wucai Wei, Wei Zhao, Guandong Xu, Hai Jin
In particular, we first explore the ways of transforming structured tabular data into sequential text prompts, as to feed them into LLMs and analyze which table content contributes most to the NL2Vis.
1 code implementation • 24 Apr 2024 • Zhaoyang Chu, Yao Wan, Qian Li, Yang Wu, Hongyu Zhang, Yulei Sui, Guandong Xu, Hai Jin
We argue that these factual reasoning-based explanations cannot answer critical what-if questions: What would happen to the GNN's decision if we were to alter the code graph into alternative structures?
1 code implementation • 24 Apr 2024 • Batu Guan, Yao Wan, Zhangqian Bi, Zheng Wang, Hongyu Zhang, Yulei Sui, Pan Zhou, Lichao Sun
As Large Language Models (LLMs) are increasingly used to automate code generation, it is often desired to know if the code is AI-generated and by which model, especially for purposes like protecting intellectual property (IP) in industry and preventing academic misconduct in education.
1 code implementation • 22 Apr 2024 • Yao Wan, Guanghua Wan, Shijie Zhang, Hongyu Zhang, Yulei Sui, Pan Zhou, Hai Jin, Lichao Sun
Subsequently, the membership classifier can be effectively employed to deduce the membership status of a given code sample based on the output of a target code completion model.
no code implementations • 25 Mar 2024 • Zhangqian Bi, Yao Wan, Zheng Wang, Hongyu Zhang, Batu Guan, Fangxin Lu, Zili Zhang, Yulei Sui, Xuanhua Shi, Hai Jin
Large language models (LLMs) have shown remarkable progress in automated code generation.
no code implementations • 20 Feb 2024 • Wei Zhao, Zhitao Hou, Siyuan Wu, Yan Gao, Haoyu Dong, Yao Wan, Hongyu Zhang, Yulei Sui, Haidong Zhang
Writing formulas on spreadsheets, such as Microsoft Excel and Google Sheets, is a widespread practice among users performing data analysis.
no code implementations • 30 Dec 2023 • Yao Wan, Yang He, Zhangqian Bi, JianGuo Zhang, Hongyu Zhang, Yulei Sui, Guandong Xu, Hai Jin, Philip S. Yu
We also benchmark several state-of-the-art neural models for code intelligence, and provide an open-source toolkit tailored for the rapid prototyping of deep-learning-based code intelligence models.
1 code implementation • 28 Apr 2023 • Jing Li, Yuangang Pan, Yueming Lyu, Yinghua Yao, Yulei Sui, Ivor W. Tsang
Unlike existing model tuning methods where the target data is always ready for calculating model gradients, the model providers in EXPECTED only see some feedbacks which could be as simple as scalars, such as inference accuracy or usage rate.
no code implementations • 31 Dec 2022 • Yunjiao Lei, Dayong Ye, Sheng Shen, Yulei Sui, Tianqing Zhu, Wanlei Zhou
A large number of studies have focused on these security and privacy problems in reinforcement learning.
no code implementations • 18 Nov 2022 • Guanqin Zhang, Jiankun Sun, Feng Xu, H. M. N. Dilum Bandara, Shiping Chen, Yulei Sui, Tim Menzies
Deep neural networks (DNNs), are widely used in many industries such as image recognition, supply chain, medical diagnosis, and autonomous driving.
1 code implementation • 14 Feb 2022 • Yao Wan, Wei Zhao, Hongyu Zhang, Yulei Sui, Guandong Xu, Hai Jin
In this paper, we conduct a thorough structural analysis aiming to provide an interpretation of pre-trained language models for source code (e. g., CodeBERT, and GraphCodeBERT) from three distinctive perspectives: (1) attention analysis, (2) probing on the word embedding, and (3) syntax tree induction.
1 code implementation • 19 Jan 2022 • Yi Gui, Yao Wan, Hongyu Zhang, Huifang Huang, Yulei Sui, Guandong Xu, Zhiyuan Shao, Hai Jin
Binary-source code matching plays an important role in many security and software engineering related tasks such as malware detection, reverse engineering and vulnerability assessment.
no code implementations • 30 Mar 2020 • Jing Li, Yuangang Pan, Yulei Sui, Ivor W. Tsang
This paper studies, for the first time, how pairwise information can be leaked to attackers during distance metric learning, and develops differential pairwise privacy (DPP), generalizing the definition of standard differential privacy, for secure metric learning.