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(Over)Reliance on Test Agents in AI-Assisted Software Testing

Fulltext:


Authors:

Eduard Paul Enoiu

Research groups:

Software Testing Laboratory
Formal Modelling and Analysis of Embedded Systems

Publication Type:

Conference/Workshop Paper

Venue:

OverReliance on Cognitive AI Systems in Safety-Critical Domains

Abstract

AI-based test agents promise to accelerate software testing by shortening feedback loops in continuous development and improving scalability and maintainability. To realize these benefits, engineers must still be able to assess if agent outputs are useful, valid, and reliable, rather than treating them as credible because they come from a capable system. This paper argues that overreliance on AI in testing is both an agency problem, in which engineers may cede cognitive control over test design decisions, and an assurance problem, in which testing artifacts may be accepted as evidence without sufficient scrutiny. We develop this argument through three theoretical lenses: software testing as cognitive problem-solving, test agents as adaptively autonomous entities, and test design argumentation as a means of making generated tests reviewable. We propose a framework for collecting data on overreliance in test agent workflows and identify specific modes of overdependence. The goal is to support accelerated testing without weakening judgment or the assurance value of testing evidence.

Bibtex

@inproceedings{Enoiu7397,
author = {Eduard Paul Enoiu},
title = {(Over)Reliance on Test Agents in AI-Assisted Software Testing},
month = {May},
year = {2026},
booktitle = {OverReliance on Cognitive AI Systems in Safety-Critical Domains},
url = {http://www.es.mdu.se/publications/7397-}
}