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Adopting Large Language Model Agents in Software Testing: A User-Centered Framework
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AI
DOI：10.1109/ms.2026.3696850.png)
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Abstract
En 中文
This article explores the human-centric factors shaping software testers’ adoption of large language model agents. Drawing on a survey, we propose an adoption framework comprising six factors to help testers evaluate and redesign testing practices for the long-term adoption of LLM agents.
Keywords:
Large language models
Modeling
Software testing
Design methodology
Feedback
Human in the loop
User centered design
Agentic AI
Human computer interaction
Creativity