Attitudes Towards LLM Use Among Software Engineering Researchers: Results From A Two-Phase Survey Study

This two-phase survey study investigates Software Engineering (SE) researchers' attitudes toward incorporating AI tools, particularly large language models (LLMs), in their workflows. Conducted at a large, independent European research institute, the study provides insights into the emerging norms and attitudes regarding AI integration within empirical SE practices, crucial insights for ensuring responsible AI use and maintaining scientific integrity in the SE field. Our findings reveal that SE researchers prefer LLM contributions when applied on small, narrowly scoped, and verifiable tasks rather than open-ended tasks, and used as a as supplementary tool to traditional methods. In contrast, it is viewed as inappropriate for evaluating others' work. Finally, we observed conflicting views regarding high-stakes tasks traditionally reflecting genuine human effort and emotional commitment.

Authors

Institutions

Publication Details

Published
2025-06-23
DOI
https://doi.org/10.1145/3696630.3731671
Primary Topic
Technology Adoption and User Behaviour
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Attitudes Towards LLM Use Among Software Engineering Researchers: Results From A Two-Phase Survey Study

Astri Barbala, Viggo Tellefsen Wivestad
Technology Adoption and User Behaviour
article

Attitudes Towards LLM Use Among Software Engineering Researchers: Results From A Two-Phase Survey Study

Astri Barbala, Viggo Tellefsen Wivestad
article en

Abstract

This two-phase survey study investigates Software Engineering (SE) researchers' attitudes toward incorporating AI tools, particularly large language models (LLMs), in their workflows. Conducted at a large, independent European research institute, the study provides insights into the emerging norms and attitudes regarding AI integration within empirical SE practices, crucial insights for ensuring responsible AI use and maintaining scientific integrity in the SE field. Our findings reveal that SE researchers prefer LLM contributions when applied on small, narrowly scoped, and verifiable tasks rather than open-ended tasks, and used as a as supplementary tool to traditional methods. In contrast, it is viewed as inappropriate for evaluating others' work. Finally, we observed conflicting views regarding high-stakes tasks traditionally reflecting genuine human effort and emotional commitment.

SINTEF (NO), SINTEF Digital
European Commission, Norges Forskningsråd, Research Executive Agency
Openalex Percentile: Top 81%
Technology Adoption and User Behaviour
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.