Model Description

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

The model performs binary text classification to determine whether a user story can be interpreted clearly without relying on vague terminology, unclear references, or abstractions that may lead to multiple plausible interpretations.

The criterion focuses on reducing ambiguity in natural-language requirements so that stakeholders can derive a sufficiently consistent interpretation of the requested functionality.

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 Unambiguous criterion.
Correct: The story expresses the requirement clearly and avoids terminology or references that introduce multiple interpretations.
Incorrect: The story contains vague, underspecified, or ambiguous expressions that may result in different interpretations.

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