Exploring LLMs for User Story Extraction from Mockups
Authors: Diego Firmenich, Leandro Antonelli, Bruno Pazos, Fabricio Lozada, Leonardo Morales
Organizations: Departamento de Informática, Facultad de Ingeniería, Universidad Nacional de la Patagonia, Argentina · LIFIA, Facultad de Informática, Universidad Nacional de La Plata, Argentina · CAETI, Facultad de Tecnología Informática - Universidad Abierta Interamericana · Laboratorio de Ciencias de la Imágenes, DIEC, UNS · UNIANDES, Universidad Regional Autónoma de los Andes, Ecuador · Instituto Patagónico de Ciencias Sociales y Humanas, CONICET-CENPAT
User stories are one of the most widely used artifacts in the software industry to define functional requirements. In parallel, the use of high-fidelity mockups facilitates end-user participation in defining their needs. In this work, we explore how combining these techniques with large language models (LLMs) enables agile and automated generation of user stories from mockups. To this end, we present a case study that analyzes the ability of LLMs to extract user stories from high-fidelity mockups, both with and without the inclusion of a glossary of the Language Extended Lexicon (LEL) in the prompts. Our results demonstrate that incorporating the LEL significantly enhances the accuracy and suitability of the generated user stories. This approach represents a step forward in the integration of AI into requirements engineering, with the potential to improve communication between users and developers.
Figures & tables
Figure 1: Proposed Collaborative Workflow
Role
Action
Tool
Product
Format
End-user
Defines requirements
High-fidelity mockup tool
WA based Mockup
WA Mockup, Images
Requirements Engineer
Defines the application domain language
LEL edition Tool
LEL
Text
Product Owner
Manages and prioritizes requirements
Kanban, Prompt
Mockup+US
Text, Image
Developer
Implements requirements
Kanban, IDEs
Artifact
Software
Table 1: Roles, Actions, Tools, Products, and Formats in the Proposed Approach
Figure 2: User Creating a High-Fidelity Mockup
Figure 3: User Creating a High-Fidelity Mockup
Figure 4: LeafLab web application displaying floristic species list, ordered by system #id.
Figure 5: Biologist’s mockup creation process for new LeafLab feature.
Figure 6: User story for the biologist mockpup, comparison without LEL (left) and with LEL (right) in the prompt.
Subject
Notion
Behavioral responses
Biologist
User of the application licensed or doctorate in biological sciences and specialized in botany.
The biologist orders the species discovered in the transects by family. The biologist orders the species discovered in the transects by form. The biologist orders the species discovered in the transects by type. The biologist orders the species discovered in the transects by points. The biologist edits the properties of a species.
Table 2: LEL portion for Subject, Notion, Behavioral responses
Object
Notion
Transect
Imaginary straight line of approximately 300 meters, on which biologists sample specimens of biological species. Each transect is composed of 300 points.
Point
At each point of the 300 points of a transect, the biologist takes a sample of a specimen of a biological species found.
Biological Species
Species found in the field campaigns carried out by biologists. Each species may have been found in 0 or more points.
Visit
A traverse of a particular transect. Each traverse of a transect involves walking approximately 300 meters, recording the species of a particular floristic specimen every 3 meters, resulting in 100 points per transect.
Table 3: LEL portion for Objects and Notions
Type of Requirement
Score without LEL
Score with LEL
A
3
5
B
3
5
C
4
4
D
3
5
E
3
5
Table 4: Type of Requirement Scores With and Without LEL