DistilBERT for User Story Quality Assessment
Collection
Resources for Fine-Tuned DistilBERT for Automated User Story Quality Assessment, including eight DistilBERT models and a refined QUS dataset. • 9 items • Updated
Model Description
This repository contains a fine-tuned DistilBERT model for the automated assessment of the Full Sentence criterion of the Quality User Story (QUS) framework.
The model performs binary text classification to determine whether a user story constitutes a grammatically complete and well-formed sentence rather than a fragment or incomplete textual expression.
The Full Sentence criterion concerns the linguistic completeness of the requirement independently of whether the requested functionality itself is appropriate.
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 Full Sentence criterion.
- Correct: The user story forms a complete, well-formed sentence.
- Incorrect: The user story is grammatically incomplete, fragmented, or does not constitute a full sentence.
This model is one of eight criterion-specific DistilBERT models developed for the individual quality criteria of the QUS framework.
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Model tree for devleoespinosa/DistilBERT-AUSQ-SL-Full-Sentence
Base model
distilbert/distilbert-base-uncased