Daniel Russo

53 papers receiving 943 citations

Daniel Russo's Hit Papers

Navigating the Complexity of Generative AI Adoption in Software Engineering 2024 · 59 citations
590+1Years since publication1020304050

Peers

Daniel Russo
Comparison fields: 5 of 123
  • Computer Science Applications 121
  • Energy Engineering and Power Technology 48
  • Information Systems 269
  • Management Information Systems 104
  • Information Systems and Management 76
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Lorna Uden United Kingdom
Marcos Kalinowski Brazil
Ning Nan China
Annika Wolff Finland
Jeremy Pitt United Kingdom
Sayed Fayaz Ahmad Pakistan
Xia Feng China
Katsumori Matsushima Japan
Eric W. Stein United States
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Citations per year

Countries citing papers authored by Daniel Russo

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Russo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Daniel Russo, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel Russo Line = papers co-authored together Daniel Russo links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 55 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2021116
2 2021101
3 202099
4 202171
5
Navigating the Complexity of Generative AI Adoption in Software Engineering
Hit paper breakdown →
202459
6
Controlling Bias in Adaptive Data Analysis Using Information Theory
201536
7 202035
8 202134
9 202231
10 202226
11 197926
12 202225
13 202224
14 201621
15 201918
16 201717
17 201815
18 201715
19 201913
20 202413

About Daniel Russo

Daniel Russo is a scholar working on Information Systems, Computer Science Applications, Management Information Systems, Artificial Intelligence and Electrical and Electronic Engineering, having authored 55 papers that have together received 994 indexed citations. Recurring topics across this work include Software Engineering Techniques and Practices (20 papers), Software Engineering Research (11 papers), Integrated Energy Systems Optimization (6 papers), Open Source Software Innovations (4 papers), Water-Energy-Food Nexus Studies (4 papers), Teaching and Learning Programming (4 papers), Big Data and Business Intelligence (4 papers) and Online Learning and Analytics (3 papers). The work is most often cited by research in Computer Science Applications (121 citations), Energy Engineering and Power Technology (48 citations), Information Systems (269 citations), Management Information Systems (104 citations) and Information Systems and Management (76 citations). Daniel Russo has collaborated with scholars based in Denmark, Italy and United States. Frequent co-authors include Klaas-Jan Stol, Paolo Ciancarini, Niels van Berkel, Paul H. P. Hanel, Wim Thiery, Sebastian Sterl, James Zou, Ann van Griensven, Celray James Chawanda and Jorge Gonçalves. Their work appears in journals such as ACM Transactions on Software Engineering and Methodology, Empirical Software Engineering, Journal of Systems and Software, Aquaculture 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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