CV

Education

B.S. in Information Science, Wuhan University, 1998
M.S. in Computer Software and Theory, Southeast University, 2004
Ph.D in Computer Science and Technology, China University of Mining and Technology, 2014

Work experience

1998-2004:
- Nantong Institute of Technology
2004- Now:
- Nantong University
2020: Visiting Scholar
- Purdue University
- Supervisor: Professor Baijian Yang
2016-2019: Postdoctoral fellow
- Nanjing University
- Supervisor: Baowen Xu
Spring 2012: Research Assistant
- China Information Security Evaluation Center

Publications

[2014] HSFal: Effective Fault Localization using Hybrid Spectrum of Full Slices and Execution Slices

Xiaolin Ju, Shujuan Jiang, Xiang Chen, Xingya Wang, Yanmei Zhang, and Heling Cao. "HSFal: Effective fault localization using hybrid spectrum of full slices and execution slices". Journal of Systems and Software, 2014, 90: 3--17

[2017] An Interactive Debugging Framework with Heuristic Graph

Xiaolin Ju, Xiang Chen, Shujuan Jiang, Junyan Qian. "An Interactive Debugging Framework with Heuristic Graph" the 2017 IEEE International Conference on Software Quality, Reliability, and Security (SATE), Harbin, China, 03-04 November, 2017.

[2017] An in-depth study of the efficiency of risk evaluation formulas for multi-fault localization

Xiaolin Ju, Xiang Chen, Yibiao Yang, Shujuan Jiang, Junyan Qian, and Baowen Xu. "An in-depth study of the efficiency of risk evaluation formulas for multi-fault localization" the 17th IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C), Harbin, China, 03-04 November, 2017

[2020] Fuzzy C-means clustering based multi-fault localization (in Chinese)

Xingya Wang, Shujuan Jiang, Pengfei Gao, Kai Lu, Li-Li Bo, Xiaolin Ju, and Yanmei Zhang. "Fuzzy C-means clustering based multi-fault localization". Chinese Journal of Computers, 2020, 43(2): 206-232.

[2020] Mulr4FL: effective fault localization of evolution software based on multivariate logistic regression model

Xiaolin Ju, Jie Qian, Zhihua Chen, Chunyu Zhao, Junyan Qian. "Mulr4FL: effective fault localization of evolution software based on multivariate logistic regression model". IEEE Access, 2020, 8: 207858-207870.

[2021] AGFL: a graph convolutional neural network-based method for fault localization

Jie Qian, Xiaolin Ju, Xiang Chen, Hao Shen, Yiheng Shen. "AGFL: a graph convolutional neural network-based method for fault localization." the IEEE 21st International Conference on Software Quality, Reliability, and Security Companion (QRS 2021), Hainan, China, December 6-10, 2021.

[2021] Improving blocking bug pair prediction via hybrid deep learning

Zhihua Chen, Xiaolin Ju, Yiheng Shen, Xiang Chen. "Improving blocking bug pair prediction via hybrid deep learning." the 21st IEEE International Conference on Software Quality, Reliability (QRS 2021) - Companion, Hainan, China, December 6-10, 2021

[2022] Can test input selection methods for deep neural network guarantee test diversity? A large-scale empirical study

Chunyu Zhao, Yanzhou Mu, Xiang Chen, Jingke Zhao, Xiaolin Ju, and Gan Wang. "Can test input selection methods for deep neural network guarantee test diversity? A large-scale empirical study". Information and Software Technology (IST), 2022, 150: 106982.

[2022] Hybrid Multiple Deep Learning Models to Boost Blocking Bug Prediction

Zhihua Chen, Xiaolin Ju, Haocheng Wang, and Xiang Chen. "Hybrid Multiple Deep Learning Models to Boost Blocking Bug Prediction". Journal of Internet Technology, 2022, 23(5):1099-1107.

[2022] SemirFL: Boosting Fault Localization via Combining Semantic Information and Information Retrieval

Shi, Xiangyu, Ju, Xiaolin, Chen, Xiang, Lu, Guilong, Xu, Mengqi. "SemirFL: Boosting Fault Localization via Combining Semantic Information and Information Retrieval."the 22nd IEEE International Conference on Software Quality, Reliability, and Security (QRS 2022) - Companion, Guangzhou, China, December 5-9, 2022.

[2022] Blocking bugs identification via binary relevance and logistic regression analysis

Zhihua Chen, Xiaolin Ju, Guilong Lu, Xiang Chen. "Blocking bugs identification via binary relevance and logistic regression analysis" 2022 IEEE 9th International Conference on Dependable Systems and Their Applications (DSA 2022), Wulumuqi, China, August 4-5, 2022.

[2023] GNet4FL: Effective fault localization via graph convolutional neural network

Jie Qian, Xiaolin Ju, and Xiang Chen. "GNet4FL: Effective fault localization via graph convolutional neural network". Automated Software Engineering, 2023, 30(2): 1--16.

[2023] EDP-BGCNN: Effective Defect Prediction via BERT-based Graph Convolutional Neural Network

Hao Shen, Xiaolin Ju, Xiang Chen, and Guang Yang. "EDP-BGCNN: Effective Defect Prediction via BERT-based Graph Convolutional Neural Network". The 47th IEEE Annual Computers, Software, and Applications Conference (COMPSAC 2023), Torino, Italy, June 26-30, 2023.

[2023] An Empirical Study of Adversarial Training in Code Comment Generation

Yiheng Shen, Xiaolin Ju, Xiang Chen, and Guang Yang. "An Empirical Study of Adversarial Training in Code Comment Generation". The 35th International Conference on Software Engineering and Knowledge Engineering (SEKE 2023), USA, July 1-10, 2023.

[2023] Assessing the Effectiveness of Vulnerability Detection via Prompt Tuning: An Empirical Study

Guilong Lu, Xiaolin Ju, Xiang Chen, Shaoyu Yang, Liang Chen, and Hao Shen. "Assessing the Effectiveness of Vulnerability Detection via Prompt Tuning: An Empirical Study". the 30th Asia-Pacific Software Engineering Conference (APSEC 2023), Seoul, Korea, December 4--7, 2023.

[2024] GRACE: Empowering LLM-based software vulnerability detection with graph structure and in-context learning

Guilong Lu, Xiaolin Ju, Xiang Chen, Wenlong Pei, and Zhilong Cai. "GRACE: Empowering LLM-based software vulnerability detection with graph structure and in-context learning". Journal of Systems and Software, 2024, 212: 112031.

[2024] Bash comment generation via data augmentation and semantic‑aware CodeBERT

Yiheng Shen, Xiaolin Ju, Xiang Chen, and Guang Yang. "Bash comment generation via data augmentation and semantic‑aware CodeBERT". Automated Software Engineering, 2024, 31(1): 1--34.

[2024] ProRLearn: Boosting Prompt Tuning-based Vulnerability Detection by Reinforcement Learning

Zilong Ren, Xiaolin Ju, Xiang Chen, and Hao Shen. "ProRLearn: Boosting Prompt Tuning-based Vulnerability Detection by Reinforcement Learning". Automated Software Engineering, 2024, 32(2): 1--35.

[2024] Ada4DP: Adversarial Enhanced Dual-attentive Aggregation Learning for Defect Prediction

Yucheng Zhang, Chenqiang Shen, Xiaolin Ju, Hao Shen and Xiang Chen. "Ada4DP: Adversarial Enhanced Dual-attentive Aggregation Learning for Defect Prediction." the 24th IEEE International Conference on Software Quality, Reliability, and Security (QRS 2024) - Companion, Cambridge, United Kingdom, July 1-5, 2024.

Xiaolin Ju

CV