"AI does not understand the real world.": AI Augmented Software Development

Generative artificial intelligence (AI) tools are increasingly embedded in software development work, reshaping how code is produced, reviewed, and maintained. Following the public release of ChatGPT and the expansion of the AI tooling ecosystem, developers now routinely encounter AI assistance in collaborative and security-sensitive contexts. Through interviews with eleven software developers familiar with generative AI tools and working across teams, we examine how developers adopt and evaluate AI in daily work, how AI use affects security-related practices, and how these tools shape collaboration and accountability. Our findings show that AI use is contingent and selective rather than habitual. Developers primarily rely on AI for low-complexity, low-accountability tasks, while deliberately limiting use in security-critical or system-defining work. Responsibility for correctness and safety is consistently framed as non-transferable leading many developers to refuse or limit AI use.

Authors

Institutions

Publication Details

Published
2026-04-13
DOI
https://doi.org/10.1145/3772363.3799079
Primary Topic
Explainable Artificial Intelligence (XAI)
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

"AI does not understand the real world.": AI Augmented Software Development

Louise Barkhuus, Eszter Vigh, Freja Sunesen
Explainable Artificial Intelligence (XAI)
article

"AI does not understand the real world.": AI Augmented Software Development

Louise Barkhuus, Eszter Vigh, Freja Sunesen
article en

Abstract

Generative artificial intelligence (AI) tools are increasingly embedded in software development work, reshaping how code is produced, reviewed, and maintained. Following the public release of ChatGPT and the expansion of the AI tooling ecosystem, developers now routinely encounter AI assistance in collaborative and security-sensitive contexts. Through interviews with eleven software developers familiar with generative AI tools and working across teams, we examine how developers adopt and evaluate AI in daily work, how AI use affects security-related practices, and how these tools shape collaboration and accountability. Our findings show that AI use is contingent and selective rather than habitual. Developers primarily rely on AI for low-complexity, low-accountability tasks, while deliberately limiting use in security-critical or system-defining work. Responsibility for correctness and safety is consistently framed as non-transferable leading many developers to refuse or limit AI use.

IT University of Copenhagen (DK)
Industry, innovation and infrastructure
Openalex Percentile: Top 26%
Explainable Artificial Intelligence (XAI)
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.