Variability-Driven User-Story Generation using LLM and Triadic Concept Analysis

• URL: http://arxiv.org/abs/2504.08666v1
• Date: Fri, 11 Apr 2025 16:15:27 GMT
• Title: Variability-Driven User-Story Generation using LLM and Triadic Concept Analysis
• Authors: Alexandre Bazin, Alain Gutierrez, Marianne Huchard, Pierre Martin, Yulin, Zhang,
• Abstract summary: A widely used Agile practice for requirements is to produce a set of user stories (also called agile product backlog'')<n>In the context of Software Product Lines, the requirements for a family of similar systems is thus a family of user-story sets, one per system.<n>We combine Triadic Concept Analysis (TCA) and Large Language Model (LLM) prompting to suggest the user-story set required to develop a new system.
• Score: 79.17703556418292
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: A widely used Agile practice for requirements is to produce a set of user stories (also called ``agile product backlog''), which roughly includes a list of pairs (role, feature), where the role handles the feature for a certain purpose. In the context of Software Product Lines, the requirements for a family of similar systems is thus a family of user-story sets, one per system, leading to a 3-dimensional dataset composed of sets of triples (system, role, feature). In this paper, we combine Triadic Concept Analysis (TCA) and Large Language Model (LLM) prompting to suggest the user-story set required to develop a new system relying on the variability logic of an existing system family. This process consists in 1) computing 3-dimensional variability expressed as a set of TCA implications, 2) providing the designer with intelligible design options, 3) capturing the designer's selection of options, 4) proposing a first user-story set corresponding to this selection, 5) consolidating its validity according to the implications identified in step 1, while completing it if necessary, and 6) leveraging LLM to have a more comprehensive website. This process is evaluated with a dataset comprising the user-story sets of 67 similar-purpose websites.

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