Chengcheng Wan

506 citations
33 papers · 253 indexed · h-index 10
Topics
Software Engineering Research (10 papers)Adversarial Robustness in Machine Learning (7 papers)Software Testing and Debugging Techniques (7 papers)

In The Last Decade

Chengcheng Wan

26 papers receiving 237 citations

Peers

Chengcheng Wan
Comparison fields: 5 of 72
  • Information Systems 90
  • Artificial Intelligence 73
  • Software 64
  • Computer Networks and Communications 45
  • Signal Processing 34
Replace Zhiyu Fan with:
Zhiyu Fan China
Alina Lazar United States
Ingolf Krueger United States
Thong Hoang Australia
Anıl Utku Türkiye
Tian Zhang China
José R. R. Viqueira Spain
Georgios Kavallieratos Norway
Daizong Ding China
Shashank Sahu India
Chengcheng Wan relative to Zhiyu Fan China Zhiyu Fan's profile →
Citations per field
00.5×1.5×2.5×
Zhiyu Fan · 1×
Citations per year

Countries citing papers authored by Chengcheng Wan

Since Specialization
Citations

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

Fields of papers citing papers by Chengcheng Wan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chengcheng Wan

This figure shows the co-authorship network connecting the top 25 collaborators of Chengcheng Wan. A scholar is included among the top collaborators of Chengcheng Wan 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 Chengcheng Wan. Chengcheng Wan 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
#WorkIndexed citations
1 0
2 1
3 1
4 19
5 0
6 0
7 0
8 42
9 2
10 3
11 14
12 10
13 0
14 4
15 1
16
{ALERT}: Accurate Learning for Energy and Timeliness
2
17
ALERT: Accurate Anytime Learning for Energy and Timeliness
1
18 14
19 28
20 6

About Chengcheng Wan

Chengcheng Wan is a scholar working on Software, Hardware and Architecture and Signal Processing, having authored 33 papers that have together received 253 indexed citations. Recurring topics across this work include Software Engineering Research (10 papers), Adversarial Robustness in Machine Learning (7 papers) and Software Testing and Debugging Techniques (7 papers). The work is most often cited by research in Software (64 citations), Information Systems (90 citations) and Signal Processing (34 citations). Chengcheng Wan has collaborated with scholars based in China, United States and Australia. Frequent co-authors include J.B. Bowles, Shan Lu, Jianping Yan, Qian Ye, Michael Maire, Henry Hoffmann, Jiahong Wen, Weijiang Li, Jiadi Yu and Shicheng Liu. Their work appears in journals such as International Journal of Cancer, Information Sciences and IEEE Transactions on Software Engineering.

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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