Daye Nam

499 total citations · 2 hit papers
14 papers, 255 citations indexed

About

Daye Nam is a scholar working on Information Systems, Artificial Intelligence and Computer Science Applications. According to data from OpenAlex, Daye Nam has authored 14 papers receiving a total of 255 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Information Systems, 5 papers in Artificial Intelligence and 5 papers in Computer Science Applications. Recurrent topics in Daye Nam's work include Software Engineering Research (9 papers), Software System Performance and Reliability (3 papers) and Software Engineering Techniques and Practices (3 papers). Daye Nam is often cited by papers focused on Software Engineering Research (9 papers), Software System Performance and Reliability (3 papers) and Software Engineering Techniques and Practices (3 papers). Daye Nam collaborates with scholars based in United States, India and Canada. Daye Nam's co-authors include Brad A. Myers, Bogdan Vasilescu, Andrew Macvean, Vincent J. Hellendoorn, Mohammad Amin Alipour, Sruti Srinivasa Ragavan, Michael Hilton, Nenad Medvidović, Youn Kyu Lee and Amber Horvath and has published in prestigious journals such as Computer.

In The Last Decade

Daye Nam

14 papers receiving 247 citations

Hit Papers

Using an LLM to Help With Code Understanding 2024 2026 2025 2024 2024 40 80 120

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daye Nam United States 7 105 92 49 45 44 14 255
Arghavan Moradi Dakhel Canada 5 90 0.9× 139 1.5× 39 0.8× 34 0.8× 64 1.5× 6 265
Vahid Majdinasab Canada 4 90 0.9× 111 1.2× 35 0.7× 28 0.6× 67 1.5× 6 247
Shraddha Barke United States 4 80 0.8× 108 1.2× 54 1.1× 12 0.3× 59 1.3× 7 214
Dominik Sobania Germany 6 159 1.5× 113 1.2× 31 0.6× 27 0.6× 76 1.7× 20 308
Seth Bernstein United States 5 128 1.2× 96 1.0× 171 3.5× 15 0.3× 42 1.0× 7 295
Andrew Tran United States 7 182 1.7× 132 1.4× 255 5.2× 21 0.5× 55 1.3× 12 426
Aamod Sane United States 9 188 1.8× 160 1.7× 25 0.5× 77 1.7× 79 1.8× 24 289
Sherif G. Aly Egypt 7 26 0.2× 61 0.7× 49 1.0× 58 1.3× 9 0.2× 54 197
Stephen Macke United States 6 104 1.0× 94 1.0× 17 0.3× 26 0.6× 33 0.8× 8 237
Doowon Kim United States 9 148 1.4× 350 3.8× 30 0.6× 73 1.6× 52 1.2× 33 470

Countries citing papers authored by Daye Nam

Since Specialization
Citations

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

Fields of papers citing papers by Daye Nam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daye Nam

This figure shows the co-authorship network connecting the top 25 collaborators of Daye Nam. A scholar is included among the top collaborators of Daye Nam 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 Daye Nam. Daye Nam is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

14 of 14 papers shown
1.
Nam, Daye, et al.. (2025). How Much Does AI Impact Development Speed? an Enterprise-Based Randomized Controlled Trial. 618–629. 2 indexed citations
2.
Nam, Daye, Andrew Macvean, Brad A. Myers, & Bogdan Vasilescu. (2024). Understanding Documentation Use Through Log Analysis: A Case Study of Four Cloud Services. 1–17. 2 indexed citations
3.
Nam, Daye, et al.. (2024). Student-AI Interaction: A Case Study of CS1 students. 1–13. 6 indexed citations
4.
Nam, Daye, Andrew Macvean, Vincent J. Hellendoorn, Bogdan Vasilescu, & Brad A. Myers. (2024). Using an LLM to Help With Code Understanding. 1–13. 126 indexed citations breakdown →
5.
Nam, Daye, et al.. (2024). Trust in Generative AI among Students: An exploratory study. 67–73. 58 indexed citations breakdown →
6.
Nam, Daye, et al.. (2023). Towards Characterizing Trust in Generative Artificial Intelligence among Students. 3–4. 6 indexed citations
7.
Nam, Daye, Brad A. Myers, Bogdan Vasilescu, & Vincent J. Hellendoorn. (2023). Improving API Knowledge Discovery with ML: A Case Study of Comparable API Methods. 1890–1906. 4 indexed citations
8.
Nam, Daye, et al.. (2022). Predictive synthesis of API-centric code. 40–49. 6 indexed citations
9.
Horvath, Amber, et al.. (2019). The Long Tail: Understanding the Discoverability of API Functionality. 8 indexed citations
10.
Nam, Daye, Amber Horvath, Andrew Macvean, Brad A. Myers, & Bogdan Vasilescu. (2019). MARBLE: Mining for Boilerplate Code to Identify API Usability Problems. 615–627. 11 indexed citations
11.
Nam, Daye & Mayank Kejriwal. (2018). How Do Organizations Publish Semantic Markup? Three Case Studies Using Public Schema.org Crawls. Computer. 51(6). 42–51. 3 indexed citations
12.
Shahbazian, Arman, Daye Nam, & Nenad Medvidović. (2018). Toward predicting architectural significance of implementation issues. 215–219. 9 indexed citations
13.
Nam, Daye, Youn Kyu Lee, & Nenad Medvidović. (2018). EVA. 53–56. 12 indexed citations
14.
Lee, Youn Kyu, et al.. (2017). SEALANT: A detection and visualization tool for inter-app security vulnerabilities in androic. 883–888. 2 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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