Goran Mauša
Impact in
- Software top 5%
- Software Reliability and Analysis Research
- Software Testing and Debugging Techniques
-
- Antimicrobial Peptides and Activities
Papers in
-
- Software Engineering Research 17
- Software 17
- Software Reliability and Analysis Research 15
- Software Testing and Debugging Techniques 6
- Co-authors
- Tihana Galinac Grbac (16 shared papers)Daniela Kalafatović (14 shared papers)Jonatan Lerga (5 shared papers)Ivan Štajduhar (5 shared papers)Bojana Dalbelo Bašić (6 shared papers)Dario Jozinović (3 shared papers)Alberto Michelini (3 shared papers)Marina Ivašić-Kos (1 shared paper)
In The Last Decade
Goran Mauša
38 papers receiving 383 citations
Peers
Comparison fields: 5 of 98
- Software 85
- Microbiology 28
- Information Systems 96
- Artificial Intelligence 79
- Computer Vision and Pattern Recognition 42
Countries citing papers authored by Goran Mauša
This map shows the geographic impact of Goran Mauša'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 Goran Mauša with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Goran Mauša more than expected).
Fields of papers citing papers by Goran Mauša
This network shows the impact of papers produced by Goran Mauša. 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 Goran Mauša. The network helps show where Goran Mauša may publish in the future.
Co-authors
The 25 scholars most cited alongside Goran Mauša, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 41 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 45 | |
| 2 | 2017 | 42 | |
| 3 | 2023 | 39 | |
| 4 | 2022 | 33 | |
| 5 | 2024 | 27 | |
| 6 | 2022 | 22 | |
| 7 | Multivariate logistic regression prediction of fault-proneness in software modules | 2012 | 14 |
| 8 | 2015 | 14 | |
| 9 | 2023 | 14 | |
| 10 | Stability of Software Defect Prediction in Relation to Levels of Data Imbalance. | 2013 | 14 |
| 11 | 2015 | 13 | |
| 12 | 2019 | 12 | |
| 13 | 2022 | 12 | |
| 14 | 2025 | 9 | |
| 15 | 2022 | 8 | |
| 16 | 2020 | 6 | |
| 17 | 2022 | 6 | |
| 18 | 2014 | 6 | |
| 19 | 2018 | 5 | |
| 20 | 2023 | 5 |
About Goran Mauša
Goran Mauša is a scholar working on Information Systems, Software, Molecular Biology, Artificial Intelligence and Microbiology, having authored 41 papers that have together received 391 indexed citations. Recurring topics across this work include Software Engineering Research (17 papers), Software Reliability and Analysis Research (15 papers), Chemical Synthesis and Analysis (9 papers), Machine Learning in Bioinformatics (6 papers), Software Testing and Debugging Techniques (6 papers), Antimicrobial Peptides and Activities (5 papers), Software System Performance and Reliability (4 papers) and Advanced Neural Network Applications (4 papers). The work is most often cited by research in Software (85 citations), Microbiology (28 citations), Information Systems (96 citations), Artificial Intelligence (79 citations) and Computer Vision and Pattern Recognition (42 citations). Goran Mauša has collaborated with scholars based in Croatia, Italy and Spain. Frequent co-authors include Tihana Galinac Grbac, Daniela Kalafatović, Jonatan Lerga, Ivan Štajduhar, Bojana Dalbelo Bašić, Dario Jozinović, Alberto Michelini, Marina Ivašić-Kos, Ernest Giralt and Višnja Katić. Their work appears in journals such as Journal of Chemical Information and Modeling, Robotics, Biomacromolecules, The Science of The Total Environment and Nature Machine Intelligence.
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.