Model Description

This repository contains a fine-tuned DistilBERT model for the automated assessment of the Minimal criterion of the Quality User Story (QUS) framework.

The model performs binary text classification to determine whether a user story contains only the information required to express its role, requested functionality, and optional rationale.

Under QUS, a user story should remain concise and should not incorporate unnecessary information, implementation details, comments, acceptance criteria, or additional content that belongs outside the core user-story statement.

The model was developed as part of the study "Fine-Tuned DistilBERT for Automated User Story Quality Assessment".

Classification task

Input: A user story written in natural language.
Output: Binary classification indicating compliance with the Minimal criterion.
Correct: The story contains only the essential role, means, and optional ends needed to express the requirement.
Incorrect: The story includes unnecessary or extraneous information beyond those core components.

This model is one of eight criterion-specific DistilBERT models developed for the individual quality criteria of the QUS framework.