Test Design and Review Argumentation in AI-Assisted Test Generation

TL;DR

This paper introduces a structured approach for representing and reviewing the reasoning behind AI-generated test cases, focusing on argumentation quality rather than test case validity.

Contribution

It proposes a taxonomy and template for characterizing test cases by their goals, claims, reasons, and evidence to improve understanding and review.

Findings

A taxonomy for AI-assisted test argumentation

A structured template for test case reasoning

Enhanced review process focusing on argument quality

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.