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

2026-07-20

AI Summary

This study addresses the risk that engineers’ overreliance on AI-powered testing agents may erode their judgment in test design and undermine the credibility of test evidence. For the first time, this issue is systematically framed as a dual challenge of “agency” and “assurance.” Integrating cognitive problem-solving theory, adaptive autonomy, and test argumentation frameworks, the work proposes a data collection framework to identify and evaluate patterns of overreliance. By introducing a test design argumentation mechanism, the approach clarifies typical dependency patterns within AI-driven testing workflows and enhances the auditability of generated tests. This provides a theoretical foundation for developing trustworthy testing processes that balance efficiency with human judgment.

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.

Problem

• overreliance
• AI-assisted testing
• test agents
• cognitive control
• assurance

Innovation

• overreliance
• test agents
• adaptive autonomy
• test design argumentation
• assurance