Xiaoguang Mao

2.8k total citations · 2 hit papers
120 papers, 1.7k citations indexed

About

Xiaoguang Mao is a scholar working on Information Systems, Software and Artificial Intelligence. According to data from OpenAlex, Xiaoguang Mao has authored 120 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 88 papers in Information Systems, 84 papers in Software and 29 papers in Artificial Intelligence. Recurrent topics in Xiaoguang Mao's work include Software Testing and Debugging Techniques (81 papers), Software Engineering Research (76 papers) and Software Reliability and Analysis Research (57 papers). Xiaoguang Mao is often cited by papers focused on Software Testing and Debugging Techniques (81 papers), Software Engineering Research (76 papers) and Software Reliability and Analysis Research (57 papers). Xiaoguang Mao collaborates with scholars based in China, Luxembourg and Singapore. Xiaoguang Mao's co-authors include Yan Lei, Yuhua Qi, Chengsong Wang, Shangwen Wang, Ziying Dai, Bo Lin, Ming Wen, Zhuo Zhang, Meng Yan and Zhuo Zhang and has published in prestigious journals such as PLoS ONE, The Science of The Total Environment and IEEE Access.

In The Last Decade

Xiaoguang Mao

109 papers receiving 1.6k citations

Hit Papers

The strength of random search on automated program repair 2014 2026 2018 2022 2014 2024 50 100 150 200
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.

  1. The strength of random search on automated program repair
    2014 212 citations Yuhua Qi, Xiaoguang Mao et al. profile →
  2. Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning
    2024 78 citations Mingyang Geng, Shangwen Wang et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiaoguang Mao China 21 1.2k 1.1k 357 327 199 120 1.7k
Terence Parr United States 14 428 0.4× 368 0.3× 170 0.5× 500 1.5× 89 0.4× 24 960
Saeed Parsa Iran 17 553 0.5× 236 0.2× 487 1.4× 223 0.7× 165 0.8× 121 1.0k
Anil Kumar Tripathi India 19 644 0.6× 389 0.4× 537 1.5× 172 0.5× 36 0.2× 95 1.1k
Wenchang Shi China 18 644 0.6× 155 0.1× 345 1.0× 531 1.6× 315 1.6× 66 1.1k
Kalyanaraman Vaidyanathan United States 13 853 0.7× 684 0.6× 1.4k 3.9× 234 0.7× 110 0.6× 15 1.7k
Arnaud Gotlieb Norway 19 472 0.4× 809 0.7× 184 0.5× 275 0.8× 66 0.3× 83 1.1k
Ketil Stølen Norway 14 639 0.6× 352 0.3× 261 0.7× 476 1.5× 116 0.6× 85 1.1k
Umberto Villano Italy 18 688 0.6× 167 0.2× 717 2.0× 352 1.1× 252 1.3× 119 1.1k
Artur Andrzejak Germany 21 738 0.6× 220 0.2× 906 2.5× 225 0.7× 116 0.6× 73 1.3k
Vittorio Cortellessa Italy 20 731 0.6× 740 0.7× 994 2.8× 809 2.5× 19 0.1× 120 1.4k

Countries citing papers authored by Xiaoguang Mao

Since Specialization
Citations

This map shows the geographic impact of Xiaoguang Mao'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 Xiaoguang Mao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaoguang Mao more than expected).

Fields of papers citing papers by Xiaoguang Mao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xiaoguang Mao. 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 Xiaoguang Mao. The network helps show where Xiaoguang Mao may publish in the future.

Co-authorship network of co-authors of Xiaoguang Mao

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaoguang Mao. A scholar is included among the top collaborators of Xiaoguang Mao 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 Xiaoguang Mao. Xiaoguang Mao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Zhao, Shan, Jie Yu, Jun Ma, et al.. (2025). How to Bridge the Gap Between Modalities: Survey on Multimodal Large Language Model. IEEE Transactions on Knowledge and Data Engineering. 37(9). 5311–5329. 6 indexed citations
2.
Xie, Huan, Lei Yan, Meng Yan, et al.. (2024). Towards More Precise Coincidental Correctness Detection With Deep Semantic Learning. IEEE Transactions on Software Engineering. 50(12). 3265–3289. 1 indexed citations
3.
4.
Liu, Kui, Yan Lei, Li Li, et al.. (2024). Demystifying API misuses in deep learning applications. Empirical Software Engineering. 29(2).
5.
Geng, Mingyang, Shangwen Wang, Dezun Dong, et al.. (2024). Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning. 1–13. 78 indexed citations breakdown →
6.
Wang, Shangwen, et al.. (2024). Divide-and-Conquer: Automating Code Revisions via Localization-and-Revision. ACM Transactions on Software Engineering and Methodology. 34(3). 1–26. 4 indexed citations
7.
Zhang, Zhuo, et al.. (2024). An effective fault localization approach for Verilog based on enhanced contexts. Frontiers of Computer Science. 18(5).
8.
9.
Zhang, Zhuo, et al.. (2023). Improving fault localization with pre-training. Frontiers of Computer Science. 18(1). 2 indexed citations
10.
Li, Panpan, et al.. (2022). Convolutional neural network-based applied research on the enrichment of heavy metals in the soil–rice system in China. Environmental Science and Pollution Research. 29(35). 53642–53655. 15 indexed citations
11.
Lei, Yan, Meng Yan, Yue Yu, et al.. (2022). Reentrancy Vulnerability Detection and Localization: A Deep Learning Based Two-phase Approach. 1–13. 38 indexed citations
12.
Zhang, Zhuo, Yan Lei, Xiaoguang Mao, Meng Yan, & Xin Xia. (2022). Improving Fault Localization Using Model-domain Synthesized Failing Test Generation. 199–210. 5 indexed citations
13.
Lei, Yan, et al.. (2022). A universal data augmentation approach for fault localization. 48–60. 32 indexed citations
14.
Li, Panpan, Yang Bai, Yuanyuan Li, et al.. (2022). Convolutional neural networks-based health risk modelling of some heavy metals in a soil-rice system. The Science of The Total Environment. 838(Pt 4). 156466–156466. 14 indexed citations
15.
Liu, Kui, Dongsun Kim, Anil Koyuncu, et al.. (2021). Where were the repair ingredients for Defects4j bugs?. Empirical Software Engineering. 26(6). 19 indexed citations
16.
Wang, Shangwen, Xiaoguang Mao, & Yue Yu. (2018). An Initial Step Towards Organ Transplantation Based on GitHub Repository. IEEE Access. 6. 59268–59281. 5 indexed citations
17.
18.
Lei, Yan, Chengsong Wang, Xiaoguang Mao, & Quanyuan Wu. (2012). Enhancing Contexts for Automated Debugging Techniques. International Conference on Software Engineering Advances. 1–7. 1 indexed citations
19.
Mao, Xiaoguang. (2010). A Classification Model for Software Trustworthiness. Jisuanji kexue yu tansuo. 9 indexed citations
20.
Mao, Xiaoguang. (2007). Aspect-Oriented Software Development:Philosophy and Observation. Computer Engineering and Science. 1 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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