Datasets:

devleoespinosa
/

qus-user-story-quality-refined

id stringlengths 10 22	story stringlengths 9 179	criterion stringclasses 8 values	label class label 2 classes
atomic-001	As a user, I want to register with a valid email address.	atomic	1correct
atomic-002	As a customer, I want to add a product to my shopping cart.	atomic	1correct
atomic-003	As a student, I want to submit an assignment.	atomic	1correct
atomic-004	As a patient, I want to schedule an appointment.	atomic	1correct
atomic-005	As a writer, I want to save a document as a PDF.	atomic	1correct
atomic-006	As a traveler, I want to search for flights.	atomic	1correct
atomic-007	As a gamer, I want to start a new game.	atomic	1correct
atomic-008	As a restaurant customer, I want to place an order.	atomic	1correct
atomic-009	As a job seeker, I want to upload my resume.	atomic	1correct
atomic-010	As a music listener, I want to create a playlist.	atomic	1correct
atomic-011	As a social media user, I want to like a post.	atomic	1correct
atomic-012	As a bank customer, I want to check my account balance.	atomic	1correct
atomic-013	As a reader, I want to bookmark a page.	atomic	1correct
atomic-014	As a video editor, I want to trim a video clip.	atomic	1correct
atomic-015	As a photographer, I want to crop an image.	atomic	1correct
atomic-016	As a musician, I want to record a sound.	atomic	1correct
atomic-017	As a shopper, I want to apply a discount code.	atomic	1correct
atomic-018	As a student, I want to view my grades.	atomic	1correct
atomic-019	As a patient, I want to request a prescription refill.	atomic	1correct
atomic-020	As a writer, I want to publish a blog post.	atomic	1correct
atomic-021	As a traveler, I want to book a hotel room.	atomic	1correct
atomic-022	As a gamer, I want to pause a game.	atomic	1correct
atomic-023	As a restaurant owner, I want to add a new menu item.	atomic	1correct
atomic-024	As a job seeker, I want to apply for a job.	atomic	1correct
atomic-025	As a music listener, I want to shuffle a playlist.	atomic	1correct
atomic-026	As a social media user, I want to follow another user.	atomic	1correct
atomic-027	As a bank customer, I want to transfer money.	atomic	1correct
atomic-028	As a reader, I want to write a comment.	atomic	1correct
atomic-029	As a video editor, I want to add text to a video.	atomic	1correct
atomic-030	As a photographer, I want to share a photo on social media.	atomic	1correct
atomic-031	As a user, I want to manage my account settings.	atomic	0incorrect
atomic-032	As a customer, I want to complete a purchase.	atomic	0incorrect
atomic-033	As a student, I want to manage my coursework.	atomic	0incorrect
atomic-034	As a patient, I want to interact with the healthcare system.	atomic	0incorrect
atomic-035	As a writer, I want to create and manage documents.	atomic	0incorrect
atomic-036	As a traveler, I want to plan a trip.	atomic	0incorrect
atomic-037	As a gamer, I want to progress through the game.	atomic	0incorrect
atomic-038	As a restaurant customer, I want to have a good dining experience.	atomic	0incorrect
atomic-039	As a job seeker, I want to find a job.	atomic	0incorrect
atomic-040	As a music listener, I want to enjoy music.	atomic	0incorrect
atomic-041	As a social media user, I want to connect with people.	atomic	0incorrect
atomic-042	As a bank customer, I want to manage my finances.	atomic	0incorrect
atomic-043	As a reader, I want to consume content.	atomic	0incorrect
atomic-044	As a video editor, I want to create professional videos.	atomic	0incorrect
atomic-045	As a photographer, I want to capture and edit photos.	atomic	0incorrect
atomic-046	As a musician, I want to create music.	atomic	0incorrect
atomic-047	As a shopper, I want to find and buy products.	atomic	0incorrect
atomic-048	As a student, I want to succeed in my studies.	atomic	0incorrect
atomic-049	As a patient, I want to get well.	atomic	0incorrect
atomic-050	As a writer, I want to be successful.	atomic	0incorrect
atomic-051	As a traveler, I want to have a great vacation.	atomic	0incorrect
atomic-052	As a gamer, I want to be entertained.	atomic	0incorrect
atomic-053	As a restaurant owner, I want to run a successful restaurant.	atomic	0incorrect
atomic-054	As a job seeker, I want to find a fulfilling career.	atomic	0incorrect
atomic-055	As a music listener, I want to discover new music.	atomic	1correct
atomic-056	As a social media user, I want to build a following.	atomic	0incorrect
atomic-057	As a bank customer, I want to achieve financial goals.	atomic	0incorrect
atomic-058	As a reader, I want to be informed and entertained.	atomic	0incorrect
atomic-059	As a video editor, I want to tell stories visually.	atomic	0incorrect
atomic-060	As a photographer, I want to express my creativity.	atomic	0incorrect
atomic-061	As a user, I want to log in, so that I can access my account.	atomic	1correct
atomic-062	As a customer, I want to view my order history, so that I can track my purchases.	atomic	1correct
atomic-063	As a student, I want to submit my assignment, so that I can get feedback.	atomic	1correct
atomic-064	As a manager, I want to approve employee leave requests, so that I can manage team availability.	atomic	1correct
atomic-065	As a shopper, I want to apply a coupon code, so that I can get a discount.	atomic	1correct
atomic-066	As a user, I want to reset my password, so that I can regain access.	atomic	1correct
atomic-067	As a customer, I want to update my address, so that I can receive my orders.	atomic	1correct
atomic-068	As a student, I want to view my grades, so that I can track my progress.	atomic	1correct
atomic-069	As a manager, I want to assign tasks, so that I can manage project workflow.	atomic	1correct
atomic-070	As a shopper, I want to add items to my cart, so that I can purchase them.	atomic	1correct
atomic-071	As a user, I want to edit my profile, so that I can update my information.	atomic	1correct
atomic-072	As a customer, I want to cancel my order, so that I can avoid unwanted purchases.	atomic	1correct
atomic-073	As a student, I want to attend a virtual class, so that I can learn remotely.	atomic	1correct
atomic-074	As a manager, I want to generate reports, so that I can analyze team performance.	atomic	1correct
atomic-075	As a shopper, I want to use guest checkout, so that I can make a quick purchase.	atomic	1correct
atomic-076	As a user, I want to enable two-factor authentication, so that I can enhance security.	atomic	1correct
atomic-077	As a customer, I want to track my order status, so that I can stay updated.	atomic	1correct
atomic-078	As a student, I want to download course materials, so that I can study offline.	atomic	1correct
atomic-079	As a manager, I want to conduct performance reviews, so that I can evaluate team members.	atomic	1correct
atomic-080	As a shopper, I want to leave product reviews, so that I can share my experience.	atomic	1correct
atomic-081	As a user, I want to customize my dashboard, so that I can personalize my view.	atomic	1correct
atomic-082	As a customer, I want to use loyalty points, so that I can redeem rewards.	atomic	1correct
atomic-083	As a student, I want to participate in discussions, so that I can engage with peers.	atomic	1correct
atomic-084	As a manager, I want to manage project timelines, so that I can ensure timely completion.	atomic	1correct
atomic-085	As a shopper, I want to get product recommendations, so that I can discover new items.	atomic	1correct
atomic-086	As a user, I want to access customer support, so that I can get help.	atomic	1correct
atomic-087	As a customer, I want to view my loyalty points balance, so that I can track my rewards.	atomic	1correct
atomic-088	As a student, I want to take quizzes, so that I can assess my knowledge.	atomic	1correct
atomic-089	As a manager, I want to assign project roles, so that I can define team responsibilities.	atomic	1correct
atomic-090	As a shopper, I want to use price filters, so that I can find products within my budget.	atomic	1correct
atomic-091	As a user, I want to log in and view my dashboard, so that I can access my account and see my profile.	atomic	0incorrect
atomic-092	As a customer, I want to place an order and track its shipping, so that I can get my products quickly.	atomic	0incorrect
atomic-093	As a student, I want to submit my assignment and get feedback, so that I can improve my grades.	atomic	0incorrect
atomic-094	As a manager, I want to approve employee leave requests and manage team workload, so that I can ensure project completion.	atomic	0incorrect
atomic-095	As a shopper, I want to apply a coupon code and get free shipping, so that I can save money.	atomic	0incorrect
atomic-096	As a user, I want to reset my password and update my security questions, so that I can regain access and enhance security.	atomic	0incorrect
atomic-097	As a customer, I want to update my address and phone number, so that I can receive my orders and get notifications.	atomic	0incorrect
atomic-098	As a student, I want to view my grades and attend a virtual class, so that I can track my progress and learn remotely.	atomic	0incorrect
atomic-099	As a manager, I want to assign tasks and manage project timelines, so that I can manage project workflow and ensure timely completion.	atomic	0incorrect
atomic-100	As a shopper, I want to add items to my cart and use guest checkout, so that I can purchase them quickly.	atomic	0incorrect

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Refined QUS User Story Quality Dataset

Dataset Summary

This repository contains a refined and relabeled adaptation of the user-story dataset reported by Sharma and Tripathi (2025) for the evaluation of user story quality according to the Quality User Story (QUS) framework.

The original corpus contains 960 criterion-specific user-story instances organized around the eight individual quality criteria of QUS. During the preparation of the experiments reported in our study, inconsistencies were identified in the original labeling. The corpus was therefore manually reviewed and relabeled using the formal definitions of the QUS criteria while preserving the original set of 960 criterion-specific instances.

This refined version is the dataset used for the experiments presented in:

Fine-Tuned DistilBERT for Automated User Story Quality Assessment
Leonardo Espinosa Arévalo, Antonio Armando Aguileta Güemez, and Raúl Antonio Aguilar Vera.
2026. Preprint manuscript.

The definitive Preprints.org DOI and citation will be added once the preprint is published.

Relationship to the Original Dataset

This dataset is not an independently created corpus.

It is derived from the dataset reported by:

Sharma, A., & Tripathi, A. K. (2025).
Evaluating user story quality with LLMs: a comparative study.
Journal of Intelligent Information Systems, 63, 1423–1451.
DOI: 10.1007/s10844-025-00939-3

The original dataset was used by Sharma and Tripathi to evaluate user story quality using generative large language models under the individual criteria of the Quality User Story framework.

Our study reuses the same set of 960 criterion-specific instances to enable a controlled comparison with specialized encoder-based Transformer models.

However, before training and evaluating the models, the labels were reviewed because inconsistencies were detected in the original annotations.

The dataset distributed in this repository corresponds to the refined labeling used in our experiments, not to the original labeling reported by Sharma and Tripathi.

Refinement and Relabeling Process

During preliminary analysis of the source dataset, cases were identified in which identical user stories appeared within the same QUS criterion with contradictory labels.

For example, some instances occurred both as compliant and non-compliant with the same quality criterion.

To address these inconsistencies, the dataset was manually reviewed using the formal definitions of the eight individual QUS criteria.

The refinement process followed these principles:

The original set of 960 criterion-specific instances was preserved.
Each individual QUS criterion was evaluated independently.
Existing labels were reviewed for semantic consistency.
Contradictory annotations were resolved.
Repeated instances were preserved when they belonged to the original corpus.
Identical story/criterion pairs no longer retain contradictory labels after refinement.
No artificial oversampling, undersampling, or other class-balancing technique was applied.

Consequently, the dataset retains the experimental structure of the source corpus while providing the corrected ground-truth labels used in our study.

Use in the Associated Study

This dataset constitutes the experimental corpus used in:

Fine-Tuned DistilBERT for Automated User Story Quality Assessment

The study investigates whether specialized encoder-based Transformer models can automatically detect violations of individual QUS quality criteria in user stories.

Three encoder architectures were experimentally evaluated:

BERT
RoBERTa
DistilBERT

An independent binary classification task was defined for each of the eight individual QUS criteria.

The DistilBERT models resulting from the study are publicly distributed through the associated Hugging Face Collection.

The dataset in this repository therefore represents the exact refined dataset on which the training, cross-validation, evaluation, and statistical analyses reported in the study are based.

QUS Criteria

The dataset covers the eight quality criteria of the QUS framework that can be evaluated independently for a single user story:

Atomic
Conceptually Sound
Estimable
Full Sentence
Minimal
Problem Oriented
Unambiguous
Well Formed

Each criterion contains exactly 120 criterion-specific instances, resulting in a total of 960 instances.

The five collective QUS criteria that require analysis across multiple user stories are outside the scope of this dataset.

Dataset Structure

Each row represents the evaluation of a user story against one specific QUS criterion.

Field	Type	Description
`id`	string	Stable identifier for the criterion-specific instance
`story`	string	User story written in natural language
`criterion`	string	QUS criterion being evaluated
`label`	class	Binary compliance label

Label Semantics

0 — Incorrect: the user story violates the evaluated QUS criterion.
1 — Correct: the user story complies with the evaluated QUS criterion.

The label should therefore be interpreted only with respect to the criterion specified in the same row.

A label of correct does not imply that the user story satisfies every QUS criterion.

Class Distribution

The following distribution corresponds to the refined labels used in the associated study.

QUS Criterion	Correct	Incorrect	Total
Atomic	61	59	120
Conceptually Sound	67	53	120
Estimable	63	57	120
Full Sentence	60	60	120
Minimal	63	57	120
Problem Oriented	55	65	120
Unambiguous	63	57	120
Well Formed	60	60	120
Total	492	468	960

No artificial class-balancing procedure was applied.

Dataset Split

The repository exposes a single split:

full

No fixed training, validation, or test partition is provided.

In the associated study, each QUS criterion was evaluated independently using five-fold cross-validation. Therefore, publishing an arbitrary fixed train/test partition would not reproduce the experimental protocol used in the paper.

Researchers wishing to reproduce the reported experiments should construct the folds from the complete dataset according to the methodology described in the associated preprint.

Associated DistilBERT Models

Eight fine-tuned DistilBERT classifiers were produced from this experimental framework, one for each individual QUS criterion:

devleoespinosa/DistilBERT-AUSQ-SL-Atomic
devleoespinosa/DistilBERT-AUSQ-SL-Conceptually-Sound
devleoespinosa/DistilBERT-AUSQ-SL-Estimable
devleoespinosa/DistilBERT-AUSQ-SL-Full-Sentence
devleoespinosa/DistilBERT-AUSQ-SL-Minimal
devleoespinosa/DistilBERT-AUSQ-SL-Problem-Oriented
devleoespinosa/DistilBERT-AUSQ-SL-Unambiguous
devleoespinosa/DistilBERT-AUSQ-SL-Well-Formed

The models and this dataset are grouped in the Hugging Face Collection associated with the study.

Intended Uses

The dataset is intended primarily for research on:

automated user story quality assessment,
requirements engineering,
natural language processing for software engineering,
binary text classification,
QUS quality-criterion detection,
evaluation of encoder-based language models,
comparison of specialized and general-purpose language models.

It may also serve as a benchmark for future approaches to automated requirements-quality assessment.

Limitations

This dataset should be interpreted considering several limitations.

First, the user stories originate from a synthetic corpus rather than from a representative sample of requirements collected directly from industrial software projects.

Second, each instance evaluates only one QUS criterion at a time. The binary label therefore represents criterion-specific compliance rather than overall user story quality.

Third, the dataset covers only the eight individual criteria of the QUS framework. Collective quality criteria requiring relationships among multiple user stories are not represented.

Finally, although the labels were manually reviewed to resolve inconsistencies in the source dataset, quality assessment in requirements engineering may still involve judgment in cases where natural-language requirements admit multiple reasonable interpretations.

Dataset Provenance

The provenance of this release can be summarized as:

Sharma & Tripathi (2025)
Original 960 criterion-specific user-story instances and original labels

↓

Manual review and relabeling
Identification and correction of semantic labeling inconsistencies according to the QUS definitions

↓

Refined QUS User Story Quality Dataset
960 instances with the corrected labels distributed in this repository

↓

Fine-Tuned DistilBERT for Automated User Story Quality Assessment (2026)
Dataset used for model fine-tuning, five-fold cross-validation, evaluation, and statistical comparison

This distinction between the source corpus and the refined experimental dataset is important for the reproducibility and interpretation of the associated results.

Associated Publication

This dataset is released as supporting research material for:

Espinosa Arévalo, L., Aguileta Güemez, A. A., & Aguilar Vera, R. A.
Fine-Tuned DistilBERT for Automated User Story Quality Assessment.
2026.

The work evaluates specialized BERT, RoBERTa, and DistilBERT classifiers against generative-model baselines for detecting violations of individual QUS criteria.

The definitive citation and DOI will be added after publication on Preprints.org.