Gemma Catolino

1.1k total citations
54 papers, 670 citations indexed

About

Gemma Catolino is a scholar working on Information Systems, Computer Science Applications and Software. According to data from OpenAlex, Gemma Catolino has authored 54 papers receiving a total of 670 indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Information Systems, 20 papers in Computer Science Applications and 16 papers in Software. Recurrent topics in Gemma Catolino's work include Software Engineering Research (33 papers), Open Source Software Innovations (19 papers) and Software Reliability and Analysis Research (13 papers). Gemma Catolino is often cited by papers focused on Software Engineering Research (33 papers), Open Source Software Innovations (19 papers) and Software Reliability and Analysis Research (13 papers). Gemma Catolino collaborates with scholars based in Italy, Netherlands and Switzerland. Gemma Catolino's co-authors include Filomena Ferrucci, Fabio Palomba, Andy Zaidman, Damian A. Tamburri, Alexander Serebrenik, Andrea De Lucia, Dario Di Nucci, Fabiano Pecorelli, Meng Yan and Francesca Arcelli Fontana and has published in prestigious journals such as IEEE Transactions on Software Engineering, IEEE Software and IEEE Transactions on Reliability.

In The Last Decade

Gemma Catolino

44 papers receiving 659 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gemma Catolino Italy 15 531 311 170 163 88 54 670
Michaela Greiler United States 15 622 1.2× 372 1.2× 173 1.0× 123 0.8× 63 0.7× 25 693
Cornelia Boldyreff United Kingdom 15 448 0.8× 114 0.4× 147 0.9× 164 1.0× 156 1.8× 84 676
Bin Lin Switzerland 13 406 0.8× 100 0.3× 176 1.0× 88 0.5× 183 2.1× 35 599
Jari Vanhanen Finland 15 569 1.1× 225 0.7× 162 1.0× 95 0.6× 81 0.9× 26 647
Rafael de Mello Brazil 14 460 0.9× 161 0.5× 128 0.8× 137 0.8× 103 1.2× 57 549
Görkem Giray Türkiye 9 326 0.6× 92 0.3× 100 0.6× 82 0.5× 83 0.9× 22 482
Sue Black United Kingdom 12 330 0.6× 129 0.4× 64 0.4× 72 0.4× 89 1.0× 43 464
Arilo Claudio Dias‐Neto Brazil 11 271 0.5× 147 0.5× 111 0.7× 75 0.5× 57 0.6× 52 388
Zijad Kurtanović Germany 7 401 0.8× 98 0.3× 55 0.3× 70 0.4× 166 1.9× 9 494
David P. Darcy United States 8 482 0.9× 294 0.9× 103 0.6× 90 0.6× 143 1.6× 12 610

Countries citing papers authored by Gemma Catolino

Since Specialization
Citations

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

Fields of papers citing papers by Gemma Catolino

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gemma Catolino

This figure shows the co-authorship network connecting the top 25 collaborators of Gemma Catolino. A scholar is included among the top collaborators of Gemma Catolino 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 Gemma Catolino. Gemma Catolino 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.
Ferrara, Carmine, et al.. (2025). Fairness-aware practices from developers’ perspective: A survey. Information and Software Technology. 182. 107710–107710. 4 indexed citations
2.
Tamburri, Damian A., Willem‐Jan van den Heuvel, Fabio Palomba, et al.. (2025). Uncovering Community Smells in Machine Learning-Enabled Systems: Causes, Effects, and Mitigation Strategies. ACM Transactions on Software Engineering and Methodology. 34(6). 1–48. 1 indexed citations
3.
Scala, Barbara, et al.. (2025). Fair and square? Evaluating fairness of LLM-generated synthetic datasets. Information and Software Technology. 191. 107980–107980.
4.
Catolino, Gemma, et al.. (2025). Examining the impact of bias mitigation algorithms on the sustainability of ML-enabled systems: A benchmark study. Journal of Systems and Software. 230. 112458–112458. 1 indexed citations
5.
Catolino, Gemma, et al.. (2025). Investigating the Role of Cultural Values in Adopting Large Language Models for Software Engineering. ACM Transactions on Software Engineering and Methodology. 35(1). 1–43. 2 indexed citations
6.
Catolino, Gemma, et al.. (2024). Few Images, Many Insights : Illicit Content Detection Using a Limited Number of Images. ACM Transactions on Intelligent Systems and Technology. 15(6). 1–26. 1 indexed citations
7.
Chen, Hongmei, et al.. (2024). An Empirical Study of Social Debt in Open-Source Projects: Social Drivers and the “Known Devil” Community Smell. Proceedings of the ... Annual Hawaii International Conference on System Sciences.
8.
9.
Palomba, Fabio, et al.. (2024). Security Risk Assessment on Cloud: A Systematic Mapping Study. 604–613. 4 indexed citations
10.
Catolino, Gemma, et al.. (2023). On the adoption and effects of source code reuse on defect proneness and maintenance effort. Empirical Software Engineering. 29(1). 2 indexed citations
11.
Kumara, Indika, Fabiano Pecorelli, Gemma Catolino, et al.. (2023). Architecting MLOps in the Cloud: From Theory to Practice. 333–335. 4 indexed citations
12.
Catolino, Gemma, et al.. (2022). "When the Code becomes a Crime Scene" Towards Dark Web Threat Intelligence with Software Quality Metrics. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 160. 439–443. 3 indexed citations
13.
Catolino, Gemma, et al.. (2022). Community Smell Detection and Refactoring in SLACK: The CADOCS Project. 469–473. 4 indexed citations
14.
Catolino, Gemma, et al.. (2022). Go Serverless With RADON! A Practical DevOps Experience Report. IEEE Software. 40(2). 80–89.
15.
Catolino, Gemma, Fabiano Pecorelli, Damian A. Tamburri, et al.. (2022). “There and Back Again?” On the Influence of Software Community Dispersion Over Productivity. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 177–184. 3 indexed citations
16.
Xu, Zhou, Tao Zhang, Meng Yan, et al.. (2021). Effort-Aware Just-in-Time Bug Prediction for Mobile Apps Via Cross-Triplet Deep Feature Embedding. IEEE Transactions on Reliability. 71(1). 204–220. 22 indexed citations
17.
Catolino, Gemma, Fabio Palomba, Francesca Arcelli Fontana, et al.. (2019). Improving change prediction models with code smell-related information. Empirical Software Engineering. 25(1). 49–95. 35 indexed citations
18.
Catolino, Gemma, Fabio Palomba, Damian A. Tamburri, Alexander Serebrenik, & Filomena Ferrucci. (2019). Gender Diversity and Women in Software Teams: How Do They Affect Community Smells?. TU/e Research Portal. 11–20. 90 indexed citations
19.
Catolino, Gemma & Filomena Ferrucci. (2019). An extensive evaluation of ensemble techniques for software change prediction. Journal of Software Evolution and Process. 31(9). 24 indexed citations
20.
Catolino, Gemma. (2017). Just-In-Time Bug Prediction in Mobile Applications: The Domain Matters!. 201–202. 23 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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