User Story Tutor (UST) to Support Agile Software Developers
- URL: http://arxiv.org/abs/2406.16259v1
- Date: Mon, 24 Jun 2024 01:55:01 GMT
- Title: User Story Tutor (UST) to Support Agile Software Developers
- Authors: Giseldo da Silva Neo, José Antão Beltrão Moura, Hyggo Oliveira de Almeida, Alana Viana Borges da Silva Neo, Olival de Gusmão Freitas Júnior,
- Abstract summary: We designed, implemented, applied, and evaluated a web application called User Story Tutor (UST)
UST checks the description of a given User Story for readability, and if needed, recommends appropriate practices for improvement.
UST may support the continuing education of agile development teams when writing and reviewing User Stories.
- Score: 0.4077787659104315
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: User Stories record what must be built in projects that use agile practices. User Stories serve both to estimate effort, generally measured in Story Points, and to plan what should be done in a Sprint. Therefore, it is essential to train software engineers on how to create simple, easily readable, and comprehensive User Stories. For that reason, we designed, implemented, applied, and evaluated a web application called User Story Tutor (UST). UST checks the description of a given User Story for readability, and if needed, recommends appropriate practices for improvement. UST also estimates a User Story effort in Story Points using Machine Learning techniques. As such UST may support the continuing education of agile development teams when writing and reviewing User Stories. UST's ease of use was evaluated by 40 agile practitioners according to the Technology Acceptance Model (TAM) and AttrakDiff. The TAM evaluation averages were good in almost all considered variables. Application of the AttrakDiff evaluation framework produced similar good results. Apparently, UST can be used with good reliability. Applying UST to assist in the construction of User Stories is a viable technique that, at the very least, can be used by agile developments to complement and enhance current User Story creation.
Related papers
- Know Me, Respond to Me: Benchmarking LLMs for Dynamic User Profiling and Personalized Responses at Scale [51.9706400130481]
Large Language Models (LLMs) have emerged as personalized assistants for users across a wide range of tasks.
PERSONAMEM features curated user profiles with over 180 simulated user-LLM interaction histories.
We evaluate LLM chatbots' ability to identify the most suitable response according to the current state of the user's profile.
arXiv Detail & Related papers (2025-04-19T08:16:10Z) - Learning to Reason for Long-Form Story Generation [98.273323001781]
We propose a general story-generation task (Next-Chapter Prediction) and a reward formulation (Verified Rewards via Completion Likelihood Improvement)
We learn to reason over a story's condensed information and generate a detailed plan for the next chapter.
Our reasoning is evaluated via the chapters it helps a story-generator create, and compared against non-trained and supervised finetuning (SFT) baselines.
arXiv Detail & Related papers (2025-03-28T18:48:26Z) - USeR: A Web-based User Story eReviewer for Assisted Quality Optimizations [2.746265158172294]
Multiple user story quality guidelines exist, but authors like Product Owners in industry projects frequently fail to write high-quality user stories.
This situation is exacerbated by the lack of tools for assessing user story quality.
We propose User Story eReviewer (USeR) a web-based tool that allows authors to determine and optimize user story quality.
arXiv Detail & Related papers (2025-03-03T21:02:10Z) - Exploring LLMs Impact on Student-Created User Stories and Acceptance Testing in Software Development [0.0]
This study investigates how LLMs (large language models) affect undergraduate software engineering students' ability to transform user feedback into user stories.
Students, working individually, were asked to analyze user feedback comments, appropriately group related items, and create user stories.
We found that LLMs help students develop valuable stories with well-defined acceptance criteria.
arXiv Detail & Related papers (2025-02-04T19:35:44Z) - Improving Ontology Requirements Engineering with OntoChat and Participatory Prompting [3.3241053483599563]
ORE has primarily relied on manual methods, such as interviews and collaborative forums, to gather user requirements from domain experts.
Current OntoChat offers a framework for ORE that utilise large language models (LLMs) to streamline the process.
This study produces pre-defined prompt templates based on user queries, focusing on creating and refining personas, goals, scenarios, sample data, and data resources for user stories.
arXiv Detail & Related papers (2024-08-09T19:21:14Z) - Interlinking User Stories and GUI Prototyping: A Semi-Automatic LLM-based Approach [55.762798168494726]
We present a novel Large Language Model (LLM)-based approach for validating the implementation of functional NL-based requirements in a graphical user interface (GUI) prototype.
Our approach aims to detect functional user stories that are not implemented in a GUI prototype and provides recommendations for suitable GUI components directly implementing the requirements.
arXiv Detail & Related papers (2024-06-12T11:59:26Z) - CELA: Cost-Efficient Language Model Alignment for CTR Prediction [71.85120354973073]
Click-Through Rate (CTR) prediction holds a paramount position in recommender systems.
Recent efforts have sought to mitigate these challenges by integrating Pre-trained Language Models (PLMs)
We propose textbfCost-textbfEfficient textbfLanguage Model textbfAlignment (textbfCELA) for CTR prediction.
arXiv Detail & Related papers (2024-05-17T07:43:25Z) - How far are AI-powered programming assistants from meeting developers' needs? [17.77734978425295]
In-IDE AI coding assistant tools (ACATs) like GitHub Copilot have significantly impacted developers' coding habits.
We simulate real development scenarios and recruit 27 computer science students to investigate their behavior with three popular ACATs.
We find that ACATs generally enhance task completion rates, reduce time, improve code quality, and increase self-perceived productivity.
arXiv Detail & Related papers (2024-04-18T08:51:14Z) - Use of Agile Practices in Start-ups [5.664445343364966]
Small, motivated teams and uncertain project scope makes start-ups good candidates for adopting Agile practices.
Use of Agile practices is associated with effects on source code and overall product quality.
A team's positive or negative attitude towards best engineering practices is a significant indicator for either adoption or rejection of certain Agile practices.
arXiv Detail & Related papers (2024-02-14T20:12:51Z) - Applying User Experience and User-Centered Design Software Processes in
Undergraduate Mobile Application Development Teaching [0.0]
We have structured an Android application development course based on a tailored agile process for development of educational software tools.
The course is executed in two phases: the first half of the course's semester presents theory on agile and mobile applications development, the latter half is managed as a workshop where students develop for an actual client.
arXiv Detail & Related papers (2023-08-14T23:20:07Z) - ChatGPT as a tool for User Story Quality Evaluation: Trustworthy Out of
the Box? [3.6526713965824515]
This study explores using ChatGPT for user story quality evaluation and compares its performance with an existing benchmark.
Our study shows that ChatGPT's evaluation aligns well with human evaluation, and we propose a best of three'' strategy to improve its output stability.
arXiv Detail & Related papers (2023-06-21T09:26:27Z) - Robust Preference Learning for Storytelling via Contrastive
Reinforcement Learning [53.92465205531759]
Controlled automated story generation seeks to generate natural language stories satisfying constraints from natural language critiques or preferences.
We train a contrastive bi-encoder model to align stories with human critiques, building a general purpose preference model.
We further fine-tune the contrastive reward model using a prompt-learning technique to increase story generation robustness.
arXiv Detail & Related papers (2022-10-14T13:21:33Z) - All You Need Is Logs: Improving Code Completion by Learning from
Anonymous IDE Usage Logs [55.606644084003094]
We propose an approach for collecting completion usage logs from the users in an IDE.
We use them to train a machine learning based model for ranking completion candidates.
Our evaluation shows that using a simple ranking model trained on the past user behavior logs significantly improved code completion experience.
arXiv Detail & Related papers (2022-05-21T23:21:26Z) - Emerging App Issue Identification via Online Joint Sentiment-Topic
Tracing [66.57888248681303]
We propose a novel emerging issue detection approach named MERIT.
Based on the AOBST model, we infer the topics negatively reflected in user reviews for one app version.
Experiments on popular apps from Google Play and Apple's App Store demonstrate the effectiveness of MERIT.
arXiv Detail & Related papers (2020-08-23T06:34:05Z) - Empowering Active Learning to Jointly Optimize System and User Demands [70.66168547821019]
We propose a new active learning approach that jointly optimize the active learning system (training efficiently) and the user (receiving useful instances)
We study our approach in an educational application, which particularly benefits from this technique as the system needs to rapidly learn to predict the appropriateness of an exercise to a particular user.
We evaluate multiple learning strategies and user types with data from real users and find that our joint approach better satisfies both objectives when alternative methods lead to many unsuitable exercises for end users.
arXiv Detail & Related papers (2020-05-09T16:02:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.