Model Description

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

The model performs binary text classification to determine whether a user story is sufficiently scoped and concrete to support effort estimation and planning.

A story violates this criterion when it describes a requirement at such a coarse level of granularity that its implementation effort cannot reasonably be estimated or prioritized without further decomposition.

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 Estimable criterion.
Correct: The requirement is sufficiently scoped to support planning and effort estimation.
Incorrect: The requirement is too broad, coarse-grained, or underspecified to be reasonably estimated.

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