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

2025-04-11

AI Summary

Automating the generation of user story sets for new systems in software product lines—based on existing system families’ variability logic—remains challenging. Method: This paper proposes a synergistic approach integrating Triadic Concept Analysis (TCA) and Large Language Model (LLM) prompt engineering. We pioneer the application of TCA to model the three-dimensional variability structure of “system–role–feature,” which guides LLMs to generate interpretable, semantically enriched, variability-aware user stories. Compliance, completeness, and consistency are ensured via option-guided prompting and multi-round validation. Results: Evaluated on a real-world dataset of 67 websites’ user stories, our method significantly improves requirement coverage (+23.6%) and cross-system consistency, while enabling traceable design decisions.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Generating user stories for new systems using variability logic
• Combining TCA and LLM to suggest design options
• Validating and completing user-story sets for similar websites

Innovation

• Combining TCA and LLM for user-story generation
• 3D variability analysis via TCA implications
• LLM-enhanced user-story consolidation and completion