Daniel Feitosa

803 total citations
38 papers, 360 citations indexed

About

Daniel Feitosa is a scholar working on Information Systems, Artificial Intelligence and Software. According to data from OpenAlex, Daniel Feitosa has authored 38 papers receiving a total of 360 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Information Systems, 15 papers in Artificial Intelligence and 11 papers in Software. Recurrent topics in Daniel Feitosa's work include Software Engineering Research (24 papers), Advanced Software Engineering Methodologies (13 papers) and Open Source Software Innovations (9 papers). Daniel Feitosa is often cited by papers focused on Software Engineering Research (24 papers), Advanced Software Engineering Methodologies (13 papers) and Open Source Software Innovations (9 papers). Daniel Feitosa collaborates with scholars based in Netherlands, Brazil and Greece. Daniel Feitosa's co-authors include Paris Avgeriou, Andrej Zwitter, Elisa Yumi Nakagawa, Jie Tan, Apostolos Ampatzoglou, Diomidis Spinellis, Katia Romero Felizardo, Alexander Chatzigeorgiou, Rosane Minghim and José Carlos Maldonado and has published in prestigious journals such as IEEE Access, Journal of Systems and Software and Information and Software Technology.

In The Last Decade

Daniel Feitosa

34 papers receiving 336 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel Feitosa Netherlands 12 191 95 82 74 46 38 360
Elda Paja Italy 10 261 1.4× 192 2.0× 63 0.8× 82 1.1× 35 0.8× 29 395
Renuka Nagpal India 9 125 0.7× 88 0.9× 55 0.7× 49 0.7× 14 0.3× 35 312
Weiqin Zou China 6 473 2.5× 128 1.3× 129 1.6× 23 0.3× 62 1.3× 13 556
Nicolas Prat France 8 121 0.6× 87 0.9× 77 0.9× 56 0.8× 10 0.2× 17 321
João Felipe Pimentel Brazil 8 235 1.2× 113 1.2× 98 1.2× 17 0.2× 28 0.6× 17 404
Harald Psaier Austria 8 101 0.5× 99 1.0× 100 1.2× 29 0.4× 15 0.3× 12 269
Shafay Shamail Pakistan 11 200 1.0× 137 1.4× 98 1.2× 30 0.4× 79 1.7× 57 379
Haslina Md Sarkan Malaysia 9 190 1.0× 117 1.2× 73 0.9× 43 0.6× 11 0.2× 30 319
Rafa E. Al-Qutaish Canada 10 168 0.9× 66 0.7× 38 0.5× 15 0.2× 80 1.7× 32 295
Damiano Torre Canada 12 180 0.9× 184 1.9× 74 0.9× 87 1.2× 76 1.7× 27 342

Countries citing papers authored by Daniel Feitosa

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Feitosa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Feitosa

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Feitosa. A scholar is included among the top collaborators of Daniel Feitosa 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 Daniel Feitosa. Daniel Feitosa 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.
2.
Feitosa, Daniel, et al.. (2024). Mining for cost awareness in the infrastructure as code artifacts of cloud-based applications: An exploratory study. Journal of Systems and Software. 215. 112112–112112. 3 indexed citations
3.
Nikolaidis, Nikos, et al.. (2024). A Comparison of the Effectiveness of ChatGPT and Co-Pilot for Generating Quality Python Code Solutions. University of Groningen research database (University of Groningen / Centre for Information Technology). 93–101. 4 indexed citations
4.
Tan, Jie, Daniel Feitosa, & Paris Avgeriou. (2023). The lifecycle of Technical Debt that manifests in both source code and issue trackers. Information and Software Technology. 159. 107216–107216. 2 indexed citations
5.
Feitosa, Daniel, et al.. (2023). Digital twins, big data governance, and sustainable tourism. Ethics and Information Technology. 25(4). 21 indexed citations
6.
Feitosa, Daniel, et al.. (2023). Technical debt management automation: State of the art and future perspectives. Information and Software Technology. 167. 107375–107375. 2 indexed citations
7.
Arvanitou, Elvira-Maria, et al.. (2023). Eclipse Open SmartCLIDE: An end-to-end framework for facilitating service reuse in cloud development. Journal of Systems and Software. 207. 111877–111877. 2 indexed citations
8.
Mittas, Nikolaos, et al.. (2023). A metrics-based approach for selecting among various refactoring candidates. Empirical Software Engineering. 29(1). 2 indexed citations
9.
Zhang, He, et al.. (2023). On measuring coupling between microservices. Journal of Systems and Software. 200. 111670–111670. 11 indexed citations
10.
Feitosa, Daniel, et al.. (2021). A systematic literature review on the use of big data for sustainable tourism. Current Issues in Tourism. 25(11). 1711–1730. 79 indexed citations
11.
Tan, Jie, Daniel Feitosa, Paris Avgeriou, & Mircea Lungu. (2020). Evolution of technical debt remediation in Python: A case study on the Apache Software Ecosystem. Journal of Software Evolution and Process. 33(4). 14 indexed citations
12.
Feitosa, Daniel, et al.. (2020). CODE reuse in practice: Benefiting or harming technical debt. Journal of Systems and Software. 167. 110618–110618. 9 indexed citations
13.
Feitosa, Daniel, Apostolos Ampatzoglou, Paris Avgeriou, Alexander Chatzigeorgiou, & Elisa Yumi Nakagawa. (2018). What can violations of good practices tell about the relationship between GoF patterns and run-time quality attributes?. Information and Software Technology. 105. 1–16. 14 indexed citations
14.
Feitosa, Daniel, Apostolos Ampatzoglou, Paris Avgeriou, & Elisa Yumi Nakagawa. (2018). Correlating Pattern Grime and Quality Attributes. IEEE Access. 6. 23065–23078. 3 indexed citations
15.
Feitosa, Daniel, et al.. (2017). Investigating the effect of design patterns on energy consumption. Journal of Software Evolution and Process. 29(2). 16 indexed citations
16.
Guessi, Milena, et al.. (2013). A checklist for evaluation of reference architectures of embedded systems. University of Groningen research database (University of Groningen / Centre for Information Technology). 451–454. 6 indexed citations
17.
Feitosa, Daniel & Elisa Yumi Nakagawa. (2012). An Investigation into Reference Architectures for Mobile Robotic Systems. International Conference on Software Engineering Advances. 465–471.
18.
Feitosa, Daniel, et al.. (2011). Current State of Reference Architectures in the Context of Agile Methodologies.. Software Engineering and Knowledge Engineering. 590–595. 2 indexed citations
19.
Feitosa, Daniel, et al.. (2010). Software Engineering in the Embedded Software and Mobile Robot Software Development: A Systematic Mapping.. Software Engineering and Knowledge Engineering. 738–741. 3 indexed citations
20.
Felizardo, Katia Romero, et al.. (2010). Reference Models and Reference Architectures Based on Service-Oriented Architecture: A Systematic Review. Lecture notes in computer science. 360–367. 14 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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