2 citations · 2 across the 5 of their papers we have counts for
6 papers · 1 filter
How Developers Discuss Generative AI: A Longitudinal Study of the Visual Studio Code Community
Panida Rumriankit, Akito Monden, Hiroki Inayoshi +4
Generative AI tools such as GitHub Copilot, ChatGPT, and coding agents have rapidly become part of everyday software development, yet little is known about how mainstream open sour…
Agent READMEs: An Empirical Study of Context Files for Agentic Coding
Worawalan Chatlatanagulchai, Hao Li, Yutaro Kashiwa +8
Agentic coding tools receive goals written in natural language, break them down into specific tasks, and write or execute code with minimal human intervention. Central to this proc…
Detecting and Characterizing Low and No Functionality Packages in the NPM Ecosystem
Napasorn Tevarut, Brittany Reid, Yutaro Kashiwa +4
Trivial packages, small modules with low functionality, are common in the npm ecosystem and can pose security risks despite their simplicity. This paper refines existing definition…
On the Use of Agentic Coding Manifests: An Empirical Study of Claude Code
Worawalan Chatlatanagulchai, Kundjanasith Thonglek, Brittany Reid +5
Agentic coding tools receive goals written in natural language as input, break them down into specific tasks, and write/execute the actual code with minimal human intervention. Key…
An Empirical Evaluation of Competitive Programming AI: A Case Study of AlphaCode
Sila Lertbanjongngam, Bodin Chinthanet, Takashi Ishio +5
AlphaCode is a code generation system for assisting software developers in solving competitive programming problems using natural language problem descriptions. Despite the advanta…
Identifying Algorithm Names in Code Comments
Jakapong Klainongsuang, Yusuf Sulistyo Nugroho, Hideaki Hata +4
For recent machine-learning-based tasks like API sequence generation, comment generation, and document generation, large amount of data is needed. When software developers implemen…