# A Framework To Improve User Story Sets Through Collaboration

**Authors:** Salih Göktuğ Köse, Fatma Başak Aydemir  
**arXiv:** [2301.10070](https://arxiv.org/abs/2301.10070)  
**Date:** 2023-01-24  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2301.10070v1/

## Key points

- Agile methodologies have gained popularity due to collaboration and customer involvement
- SCOUT system utilizes NLP techniques to extract key concepts from user stories and construct a knowledge graph
- The knowledge graph is used to generate suggestions for stakeholders, enhancing the completeness of user story sets
- SCOUT includes a web interface, NLP pipeline, graph module, and suggestion module
- Evaluation results show that SCOUT significantly improves the quality and completeness of user story sets
- The research provides a collaborative requirements editor with NLP-powered suggestion functionality
- Resources such as user story sets and source code are publicly available for further research.

## Summary

Agile methodologies have gained popularity in recent years due to their emphasis on collaboration and customer involvement. To address the challenge of generating complete and high-quality user story sets in collaborative environments, the proposed SCOUT system utilizes natural language processing (NLP) techniques to extract key concepts from user stories and construct a knowledge graph. This knowledge graph is then used to generate suggestions for stakeholders, enhancing the completeness of the user story sets. The system includes a web interface for easy requirements artifact construction, an NLP pipeline for concept extraction, a graph module for constructing knowledge graphs, and a suggestion module that employs heuristics to enhance completeness. The paper also provides publicly available user story sets and source code. Evaluation results indicate that SCOUT significantly improves the quality and completeness of user story sets. Overall, this research contributes to the field by providing a collaborative requirements editor with NLP-powered suggestion functionality and sharing resources for further user story research.