ENHANCING END-TO-END USER STORY CREATION WITH OPEN-SOURCE LARGE LANGUAGE MODELS: A GUIDED CHAIN-OF-THOUGHT METHOD

Andrius Dalisanskis, Enda Fallon

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

Abstract

This paper presents the results from a study aimed at enhancing agile project management through the automated creation and integration of user stories using open-source large language models (LLMs) with a proposed Guided Chain-of-Thought prompting framework. The study evaluates the effectiveness of this approach in reducing manual workload and improving user satisfaction. Findings demonstrate significant efficiency gains and positive user feedback, indicating the potential for broad adoption in real-world agile environments.

Original languageEnglish
Title of host publicationProceedings of the International Conferences on Applied Computing and WWW/Internet 2024
EditorsPaula Miranda, Pedro Isaias, Pedro Isaias, Luis Rodrigues
PublisherIADIS Press
Pages143-150
Number of pages8
ISBN (Electronic)9789898704627
Publication statusPublished - 2024
Event21st International Conference on Applied Computing 2024, AC 2024 and 23rd International Conference on WWW/Internet 2024, ICWI 2024 - Zagreb, Croatia
Duration: 26 Oct 202428 Oct 2024

Publication series

NameProceedings of the International Conferences on Applied Computing and WWW/Internet 2024

Conference

Conference21st International Conference on Applied Computing 2024, AC 2024 and 23rd International Conference on WWW/Internet 2024, ICWI 2024
Country/TerritoryCroatia
CityZagreb
Period26/10/2428/10/24

Keywords

  • LLM
  • Prompt Engineering
  • User Story Automation

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