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Marie Salomon

dblp:419/6415 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2026
0009-0002-8797-9567ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Empirical software engineering · 100%
Human-computer interaction and pervasive computing
1 paper
Collaborative and social computing · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Collaborative and social computing › computer-supported cooperative work
developer collaboration
1.012026
From Disruptions to Discussions: How GenAI Impacts Human Interactions in Software Development · IEEE Trans. Software Eng. 2026
Empirical software engineering
developer studies
1.012026
From Disruptions to Discussions: How GenAI Impacts Human Interactions in Software Development · IEEE Trans. Software Eng. 2026
Empirical software engineering › software engineering research methodology
experience sampling
0.312026
From Disruptions to Discussions: How GenAI Impacts Human Interactions in Software Development · IEEE Trans. Software Eng. 2026

Methods — techniques the papers use, named apart from their topics

survey · 2.0mixed-method study · 2.0interviews · 2.0experience sampling · 2.0
YearPublicationVenuePosition
2026 From Disruptions to Discussions: How GenAI Impacts Human Interactions in Software Development
abstract
New technologies often change how an individual performs work, such as how generative AI (GenAI) can help a developer write code. New technologies can also impact how people interact with one another, such as how GenAI’s ability to summarize API documentation can reduce the need for developers to ask each other technical questions. In this paper, we report on a two-phase mixed-method study exploring how GenAI influences how humans interact in software development. During phase one, 30 industrial software developers provided data over a period of 5 to 12 days as they worked, contributing 627 experience sampling responses and 207 end-of-workday survey responses. To gain further insight into their work, we interviewed 22 of these developers. During phase two, 131 additional professional developers responded to a survey to explore whether and how the results from phase one are seen across a larger population. Our analysis of the data found that (1) the ability of GenAI to help answer low-level technical questions in a timely way enables developers to see GenAI as a technical mentor, providing opportunities for developers to turn to tools rather than teammates; (2) developers perceive that GenAI can help them experience more focus and experience fewer flow disruptions; (3) GenAI can help developers pursue more meaningful conversations with their colleagues by shifting human interaction towards clarification, joint reasoning, and exploring alternative perspectives; and (4) in the presence of GenAI, developers report still seeking human-to-human interaction for contextual expertise, mentorship, and social connection. Together, these findings showwhat changes,when it changes, andwhat teams can do nextin response to this shift in team interaction dynamics, where GenAI increasingly handles routine technical queries and human conversations center on context, reasoning, and connection. Teams can adopt norms for delegating questions, sustain human judgment in complex decisions, and create space for expertise, mentorship, and connection, alongside increasing technical self-sufficiency.
Marie Salomon, Ekaterina Koshchenko, Agnia Sergeyuk, Reid Holmes, Gail C. Murphy, Thomas Fritz 0001
IEEE Trans. Software Eng.1