Brian Testa
Impact in
- Software top 5%
- Software Testing and Debugging Techniques
- Information Systems top 5%
- Software Engineering Research
- Web Application Security Vulnerabilities
Papers in
-
- Privacy-Preserving Technologies in Data 4
- Security and Verification in Computing 2
- Adversarial Robustness in Machine Learning 1
- Software 1
- Software Testing and Debugging Techniques 1
- Co-authors
- Rundong Zhou (1 shared paper)Yao Cheng (1 shared paper)Feng Qian (1 shared paper)Heng Yin (2 shared papers)Asif Salekin (1 shared paper)Fred Lu (3 shared papers)Edward Raff (3 shared papers)Tyler LeBlond (2 shared papers)
- Journals
- Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (1 paper)Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (1 paper)arXiv (Cornell University) (1 paper)ACM SIGSAC Conference on Computer and Communications Security (CCS) (1 paper)neural information processing systems (1 paper)
- Partner nations
- United States
In The Last Decade
Brian Testa
4 papers receiving 318 citations
Brian Testa's Hit Papers
Peers
Comparison fields: 5 of 18
- Software 78
- Information Systems 216
- Signal Processing 65
- Artificial Intelligence 154
- Computer Networks and Communications 33
Countries citing papers authored by Brian Testa
This map shows the geographic impact of Brian Testa'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 Brian Testa with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian Testa more than expected).
Fields of papers citing papers by Brian Testa
This network shows the impact of papers produced by Brian Testa. 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 Brian Testa. The network helps show where Brian Testa may publish in the future.
Co-authors
The 9 scholars most cited alongside Brian Testa, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Scalable Graph-based Bug Search for Firmware Images Hit paper breakdown → | 2016 | 326 |
| 2 | 2023 | 9 | |
| 3 | 2023 | 4 | |
| 4 | 2023 | 1 | |
| 5 | 2019 | 0 | |
| 6 | 2022 | 0 |
About Brian Testa
Brian Testa is a scholar working on Artificial Intelligence, Software, Information Systems, Nuclear Energy and Engineering and Waste Management and Disposal, having authored 6 papers that have together received 340 indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (4 papers), Security and Verification in Computing (2 papers), Adversarial Robustness in Machine Learning (1 paper), Software Engineering Research (1 paper) and Software Testing and Debugging Techniques (1 paper). The work is most often cited by research in Software (78 citations), Information Systems (216 citations), Signal Processing (65 citations), Artificial Intelligence (154 citations) and Computer Networks and Communications (33 citations). Brian Testa has collaborated with scholars based in United States. Frequent co-authors include Rundong Zhou, Yao Cheng, Feng Qian, Heng Yin, Asif Salekin, Fred Lu, Edward Raff, Tyler LeBlond and Xunchao Hu. Their work appears in journals such as Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies, Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, arXiv (Cornell University), ACM SIGSAC Conference on Computer and Communications Security (CCS) and neural information processing systems.
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.