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AI Generates, Model-Based Testing Checks: A Model-Based Approach to Validating AI-Generated Test Cases

Authors:

Eduard Paul Enoiu

Research groups:

Software Testing Laboratory
Formal Modelling and Analysis of Embedded Systems

Publication Type:

Conference/Workshop Paper

Venue:

Digital Twin Experiences and Model-Based Testing Methods @ AEiC 2026

Abstract

AI agents can increasingly generate executable tests from code, requirements, and human-written natural-language prompts, but the resulting tests often remain difficult to trust. They may compile and run, yet remain arbitrary or inconsistent with the intended system behavior. This paper proposes using Model-Based Testing (MBT) to check and classify AI-generated tests. We treat these generated software artifacts as candidate tests that must be checked against an MBT model. We define checks for the model path, guards, inputs, adequacy, and MBT logs. One can then classify candidate tests as model-backed, model-extending, or model-inconsistent. The contribution is a conceptual framework for making AI-assisted test generation more reviewable and reusable in quality assurance.

Bibtex

@inproceedings{Enoiu7400,
author = {Eduard Paul Enoiu},
title = {AI Generates, Model-Based Testing Checks: A Model-Based Approach to Validating AI-Generated Test Cases},
month = {June},
year = {2026},
booktitle = {Digital Twin Experiences and Model-Based Testing Methods @ AEiC 2026},
url = {http://www.es.mdu.se/publications/7400-}
}