(Over)Reliance on Test Agents in AI-Assisted Software Testing
2026-07-20 • Software Engineering
Software Engineering
AI summaryⓘ
The authors explain that using AI to help with software testing can make checking software faster and easier. However, they warn that engineers need to be careful not to blindly trust the AI's results without checking if they're actually useful and correct. They explore this problem by looking at how testing is like solving puzzles, how AI test tools act independently, and how to make AI-generated tests easier to review. They also suggest ways to spot when people rely too much on AI during testing. Overall, their work aims to help speed up testing without losing careful judgment or trust in the test results.
AI-based test agentssoftware testingoverreliancecontinuous developmentcognitive controladaptive autonomytest design argumentationfeedback loopsassurancetest evidence
Authors
Eduard Paul Enoiu
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.