TAGRAM: A Framework for Tagging User Stories

Maxim Bragilovski, Shahaf Erez, Chen Mordehai, Shani Rahamim, Noa Shpack, Arnon Sturm

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

User stories are a popular notation for representing requirements, especially in agile development. However, how they can be used further during the development process is still under examination. In recent years, user stories research has focused on how to apply machine learning to user stories to understand their quality, the required implementation efforts, and the ability to generate software artifacts. One primary concern in this direction is the lack of datasets and specially labeled ones. To bridge this gap, in this work, we introduce a framework for tagging user stories to construct labeled datasets for user stories for various tasks.

Original languageEnglish
Title of host publicationProceedings - 31st IEEE International Requirements Engineering Conference Workshops, REW 2023
EditorsKurt Schneider, Fabiano Dalpiaz, Jennifer Horkoff
PublisherInstitute of Electrical and Electronics Engineers
Pages62-66
Number of pages5
ISBN (Electronic)9798350326918
DOIs
StatePublished - 1 Jan 2023
Event31st IEEE International Requirements Engineering Conference Workshops, REW 2023 - Hannover, Germany
Duration: 4 Sep 20238 Sep 2023

Publication series

NameProceedings - 31st IEEE International Requirements Engineering Conference Workshops, REW 2023

Conference

Conference31st IEEE International Requirements Engineering Conference Workshops, REW 2023
Country/TerritoryGermany
CityHannover
Period4/09/238/09/23

Keywords

  • Dataset
  • Machine Learning
  • Tagging Tool
  • User Stories

ASJC Scopus subject areas

  • Organizational Behavior and Human Resource Management
  • Software
  • Safety, Risk, Reliability and Quality
  • Developmental and Educational Psychology
  • Education

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