2 citations · 2 across the 5 of their papers we have counts for
7 papers
Developer Interaction Patterns with Proactive AI: A Five-Day Field Study
Nadine Kuo, Agnia Sergeyuk, Valerie Chen +1
Current in-IDE AI coding tools typically rely on time-consuming manual prompting and context management, whereas proactive alternatives that anticipate developer needs without expl…
Does In-IDE Calibration of Large Language Models work at Scale?
Roham Koohestani, Agnia Sergeyuk, David Gros +4
The introduction of large language models into integrated development environments (IDEs) is revolutionizing software engineering, yet it poses challenges to the usefulness and rel…
Prompt-with-Me: in-IDE Structured Prompt Management for LLM-Driven Software Engineering
Ziyou Li, Agnia Sergeyuk, Maliheh Izadi
Large Language Models are transforming software engineering, yet prompt management in practice remains ad hoc, hindering reliability, reuse, and integration into industrial workflo…
Understanding Prompt Programming Tasks and Questions
Jenny T. Liang, Chenyang Yang, Agnia Sergeyuk +2
Prompting foundation models (FMs) like large language models (LLMs) have enabled new AI-powered software features (e.g., text summarization) that previously were only possible by f…
AI in Software Engineering: Perceived Roles and Their Impact on Adoption
Ilya Zakharov, Ekaterina Koshchenko, Agnia Sergeyuk
This paper investigates how developers conceptualize AI-powered Development Tools and how these role attributions influence technology acceptance. Through qualitative analysis of 3…
From Teacher to Colleague: How Coding Experience Shapes Developer Perceptions of AI Tools
Ilya Zakharov, Ekaterina Koshchenko, Agnia Sergeyuk
AI-assisted development tools promise productivity gains and improved code quality, yet their adoption among developers remains inconsistent. Prior research suggests that professio…