Test Design and Review Argumentation in AI-Assisted Test Generation

2026-04-24

AI Summary

This study addresses the lack of explicit justification in AI-generated test cases, which hinders engineers’ ability to understand and evaluate their rationale. To bridge this gap, the work introduces argumentation structures into AI-based test generation for the first time, proposing a conceptual taxonomy and a structured template grounded in four core elements: test objectives, claims, reasons, and evidence. By integrating conceptual modeling with argumentation theory, the approach establishes an interpretable test generation framework that standardizes the articulation of justifications. This mechanism substantially enhances the understandability, traceability, and trustworthiness of AI-generated test cases, enabling more effective human review and validation in software testing processes.

Technology Category

• Knowledge Representation and Reasoning: Argumentation
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

AI assistants can increasingly generate and evolve test cases. The challenge is no longer merely to produce them, but also to help engineers understand why a generated artefact exists and what supports it. Existing work has focused on classifying testing techniques, linking requirements to tests and structuring system assurance arguments, but it does not explicitly represent the argumentation behind individual test design decisions. We propose a conceptual taxonomy and a structured template for AI-assisted test generation that characterizes a test case by its test goal, claim, reason, and evidence. The taxonomy is intended for both constructive use during test design and retrospective use during review, to assess the quality of the attached argument rather than the plausibility or objective value of the generated test cases.

Problem

• AI-assisted test generation
• test design argumentation
• test case justification
• software testing
• assurance argument

Innovation

• AI-assisted test generation
• test design argumentation
• structured argument template
• test case justification
• assurance argumentation