132 citations · 239 across the 18 of their papers we have counts for
19 papers
Code in Space: How Multimodal Human-AI Experience Can Reshape the Future of Tech Creation
Ilya Zakharov, Ekaterina Koshchenko, Agnia Sergeyuk
Advances in artificial intelligence (AI) continue to reshape digital product development, yet the day-to-day tools for developers and designers remain bound to flat screens and 2D…
Configurable AI Coding Assistants: Designing For Developers Who Like to Be in Control
Ekaterina Koshchenko, Jovana Stankovic, Ilya Zakharov +1
AI coding assistants are now widely used in professional development, yet they offer only limited ways for developers to control how they behave. In this paper, we investigate what…
Trust-Calibrated Code Review: A Participatory Design Study of Review Workflows for LLM-Generated Multi-File Changes
Lo Gullstrand Heander, Agnia Sergeyuk, Ilya Zakharov +2
Background: Developers increasingly review multi-file code changes generated by LLM-based agents, yet no validated end-to-end workflow or IDE tooling design exists for this scenari…
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…
Evolving with AI: A Longitudinal Analysis of Developer Logs
Agnia Sergeyuk, Eric Huang, Dariia Karaeva +3
AI-powered coding assistants are rapidly becoming fixtures in professional IDEs, yet their sustained influence on everyday development remains poorly understood. Prior research has…
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…
Human-AI Experience in Integrated Development Environments: A Systematic Literature Review
Agnia Sergeyuk, Ilya Zakharov, Ekaterina Koshchenko +1
The integration of Artificial Intelligence (AI) into Integrated Development Environments (IDEs) is reshaping software development, fundamentally altering how developers interact wi…
Developer Needs and Feasible Features for AI Assistants in IDEs
Agnia Sergeyuk, Ekaterina Koshchenko, Ilya Zakharov +2
Despite the increasing presence of AI assistants in Integrated Development Environments (IDEs), it remains unclear what different groups of developers actually need from these tool…
Assessing Consensus of Developers' Views on Code Readability
Agnia Sergeyuk, Olga Lvova, Sergey Titov +3
The rapid rise of Large Language Models (LLMs) has changed software development, with tools like Copilot, JetBrains AI Assistant, and others boosting developers' productivity. Howe…
Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forward
Agnia Sergeyuk, Yaroslav Golubev, Timofey Bryksin +1
Context. The last several years saw the emergence of AI assistants for code - multi-purpose AI-based helpers in software engineering. As they become omnipresent in all aspects of s…
A Design Space for Intelligent and Interactive Writing Assistants
Mina Lee, Katy Ilonka Gero, John Joon Young Chung +33
In our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities. We seek to addr…
Reassessing Java Code Readability Models with a Human-Centered Approach
Agnia Sergeyuk, Olga Lvova, Sergey Titov +4
To ensure that Large Language Models (LLMs) effectively support user productivity, they need to be adjusted. Existing Code Readability (CR) models can guide this alignment. However…
In-IDE Human-AI Experience in the Era of Large Language Models; A Literature Review
Agnia Sergeyuk, Sergey Titov, Maliheh Izadi
Integrated Development Environments (IDEs) have become central to modern software development, especially with the integration of Artificial Intelligence (AI) to enhance programmin…
Overcoming the Mental Set Effect in Programming Problem Solving
Agnia Sergeyuk, Sergey Titov, Yaroslav Golubev +1
This paper adopts a cognitive psychology perspective to investigate the recurring mistakes in code resulting from the mental set (Einstellung) effect. The Einstellung effect is the…
What Writing Assistants Can Learn from Programming IDEs
Sergey Titov, Agnia Sergeyuk, Timofey Bryksin
With the development of artificial intelligence, writing assistants (WAs) are changing the way people interact with text, creating lengthy outputs that can be overwhelming for user…