---
language:
- en
library_name: transformers
pipeline_tag: text-classification
base_model: distilbert/distilbert-base-uncased
base_model_relation: finetune
datasets:
- devleoespinosa/qus-user-story-quality-refined
tags:
- distilbert
- transformers
- text-classification
- requirements-engineering
- software-engineering
- user-stories
- qus
- quality-assessment
- natural-language-processing
---

## Model Description

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

The model performs binary text classification to determine whether a user story describes the user's need or desired outcome without prescribing a specific technical solution or implementation mechanism.

According to QUS, user stories should focus on the problem to be solved. Implementation choices should generally remain outside the core requirement so that the development team retains flexibility in determining how the requested functionality should be realized.

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 Problem Oriented criterion.
- **Correct:** The story expresses the user's problem, need, or desired functionality without prescribing its implementation.
- **Incorrect:** The story specifies a particular technical solution, technology, component, or implementation mechanism as part of the requirement.

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