Xiaolin Ju

480 total citations · 1 hit paper
38 papers, 278 citations indexed

About

Xiaolin Ju is a scholar working on Information Systems, Software and Computer Networks and Communications. According to data from OpenAlex, Xiaolin Ju has authored 38 papers receiving a total of 278 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Information Systems, 25 papers in Software and 9 papers in Computer Networks and Communications. Recurrent topics in Xiaolin Ju's work include Software Engineering Research (23 papers), Software Testing and Debugging Techniques (20 papers) and Software Reliability and Analysis Research (19 papers). Xiaolin Ju is often cited by papers focused on Software Engineering Research (23 papers), Software Testing and Debugging Techniques (20 papers) and Software Reliability and Analysis Research (19 papers). Xiaolin Ju collaborates with scholars based in China, Singapore and United States. Xiaolin Ju's co-authors include Xiang Chen, Shujuan Jiang, Yanmei Zhang, Zhanqi Cui, Xingya Wang, Dun Zhang, Jie Qian, Qing Gu, Xiang Gu and Bin Zhang and has published in prestigious journals such as IEEE Access, Neural Computing and Applications and Journal of Systems and Software.

In The Last Decade

Xiaolin Ju

34 papers receiving 267 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

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

2024 46 citations Xiaolin Ju, Xiang Chen et al. Journal of Systems and Software profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Xiaolin Ju China	9	202	180	78	53	50	38	278
Syed Shariyar Murtaza Canada	9	147 0.7×	93 0.5×	134 1.7×	85 1.6×	77 1.5×	25	245
Yannic Noller Singapore	7	169 0.8×	192 1.1×	36 0.5×	47 0.9×	77 1.5×	21	247
Bao N. Nguyen United States	4	147 0.7×	220 1.2×	77 1.0×	29 0.5×	78 1.6×	5	270
Marina Polishchuk United States	6	177 0.9×	178 1.0×	116 1.5×	55 1.0×	72 1.4×	8	256
Yingquan Zhao China	9	247 1.2×	259 1.4×	90 1.2×	66 1.2×	56 1.1×	18	322
Jeho Oh United States	6	120 0.6×	84 0.5×	75 1.0×	107 2.0×	17 0.3×	7	176
Rodolphe Ortalo France	3	181 0.9×	52 0.3×	189 2.4×	34 0.6×	97 1.9×	3	238
Simon Holm Jensen Denmark	5	258 1.3×	233 1.3×	64 0.8×	100 1.9×	124 2.5×	5	332
Michael Gegick United States	8	302 1.5×	198 1.1×	74 0.9×	64 1.2×	136 2.7×	16	335
Dongdong She United States	7	110 0.5×	138 0.8×	49 0.6×	86 1.6×	155 3.1×	13	241

Countries citing papers authored by Xiaolin Ju

Since Specialization

Citations

This map shows the geographic impact of Xiaolin Ju's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Xiaolin Ju with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaolin Ju more than expected).

Fields of papers citing papers by Xiaolin Ju

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xiaolin Ju. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Xiaolin Ju. The network helps show where Xiaolin Ju may publish in the future.

Co-authorship network of co-authors of Xiaolin Ju

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaolin Ju. A scholar is included among the top collaborators of Xiaolin Ju based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Xiaolin Ju. Xiaolin Ju is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Ju, Xiaolin, et al.. (2025). Improving distributed learning-based vulnerability detection via multi-modal prompt tuning. Journal of Systems and Software. 226. 112442–112442. 1 indexed citations

2.

Chen, Xiang, et al.. (2025). Evaluating and improving LLM-based competitive program generation. Information and Software Technology. 191. 107977–107977.

3.

Fan, Yingfang, et al.. (2025). Corrosion characteristics and critical corrosion depth model of reinforcement at concrete cover cracking in an electrochemical accelerated corrosion environment. Case Studies in Construction Materials. 22. e04628–e04628. 1 indexed citations

4.

Zhang, Yucheng, et al.. (2025). HGAN4VD: Leveraging Heterogeneous Graph Attention Networks for enhanced Vulnerability Detection. Computers & Security. 157. 104548–104548.

5.

Ju, Xiaolin, et al.. (2025). JIT-CF: Integrating contrastive learning with feature fusion for enhanced just-in-time defect prediction. Information and Software Technology. 182. 107706–107706.

6.

Ju, Xiaolin, et al.. (2024). GRACE: Empowering LLM-based software vulnerability detection with graph structure and in-context learning. Journal of Systems and Software. 212. 112031–112031. 46 indexed citations breakdown →

7.

Ju, Xiaolin, et al.. (2024). Bash comment generation via data augmentation and semantic-aware CodeBERT. Automated Software Engineering. 31(1). 3 indexed citations

8.

Zhang, Yucheng, et al.. (2024). Ada4DP: Adversarial Enhanced Dual-Attentive Aggregation Learning for Defect Prediction. 709–718. 1 indexed citations

9.

Shen, Hao, et al.. (2023). EDP-BGCNN: Effective Defect Prediction via BERT-based Graph Convolutional Neural Network. 850–859. 2 indexed citations

10.

Shi, Xiangyu, et al.. (2022). SemirFL: Boosting Fault Localization via Combining Semantic Information and Information Retrieval. 324–332. 2 indexed citations

11.

Chen, Zhihua, et al.. (2021). Improving Blocking Bug Pair Prediction via Hybrid Deep Learning. 16. 727–732. 1 indexed citations

12.

Qian, Jie, et al.. (2021). AGFL: A Graph Convolutional Neural Network-Based Method for Fault Localization. 672–680. 6 indexed citations

13.

Chen, Xiang, et al.. (2020). Empirical studies on the impact of filter‐based ranking feature selection on security vulnerability prediction. IET Software. 15(1). 75–89. 18 indexed citations

14.

Zhang, Bin, et al.. (2019). An Empirical Study on Combining Source Selection and Transfer Learning for Cross-Project Defect Prediction. 29–38. 16 indexed citations

15.

Wang, Xingya, et al.. (2016). Distance-Based Test-Suite Reduction for Efficient Testing-Based Fault Localization. 10. 84–89. 1 indexed citations

16.

Ju, Xiaolin. (2015). Measuring the Importance of Classes Using UIO Sequence. Dianzi xuebao. 4 indexed citations

17.

Ju, Xiaolin. (2015). Fault localization approach for null pointer exception. Journal of Communications. 1 indexed citations

18.

Jiang, Shujuan, et al.. (2015). Measuring the I mportance of Classes Using UIO Sequence. 43(10). 2062–2068. 6 indexed citations

19.

Chen, Xiang, et al.. (2014). Survey of Test Case Prioritization Techniques for Regression Testing. Journal of Software. 24(8). 1695–1712. 12 indexed citations

20.

Liu, Yingqi, Wei Li, Shujuan Jiang, Yanmei Zhang, & Xiaolin Ju. (2013). An Approach for Fault Localization Based on Program Slicing and Bayesian. 326–332. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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