Early-Stage Product Line Validation Using LLMs: A Study on Semi-Formal Blueprint Analysis

2026-04-22

AI Summary

This study addresses the lack of effective validation methods for semi-formal blueprints in early-stage software product line engineering, which often leads to undetected structural and constraint-related errors in feature models. For the first time, it systematically evaluates the capability of large language models (LLMs) in feature model analysis tasks by leveraging twelve state-of-the-art LLMs and sixteen standard analytical operations that integrate structural parsing with constraint reasoning. Performance is benchmarked against the solver-based tool FLAMA. Results demonstrate that reasoning-optimized models—such as Grok 4 Fast Reasoning and Gemini 2.5 Pro—achieve average accuracies of 88–89%, approaching the performance of formal solvers. These findings substantiate the feasibility and practical potential of LLMs as lightweight, early-stage validation tools for feature model verification.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

We study whether Large Language Models (LLMs) can perform feature model analysis operations (AOs) directly on semi-formal textual blueprints, i.e., concise constrained-language descriptions of feature hierarchies and constraints, enabling early validation in Software Product Line scoping. Using 12 state-of-the-art LLMs and 16 standard AOs, we compare their outputs against the solver-based oracle FLAMA. Results show that reasoning-optimized models (e.g., Grok 4 Fast Reasoning, Gemini 2.5 Pro) achieve 88-89% average accuracy across all evaluated blueprints and operations, approaching solver correctness. We identify systematic errors in structural parsing and constraint reasoning, and highlight accuracy-cost trade-offs that inform model selection. These findings position LLMs as lightweight assistants for early variability validation.

Problem

• Software Product Line
• Feature Model Analysis
• Semi-Formal Blueprint
• Early-Stage Validation
• Large Language Models

Innovation

• Large Language Models
• Software Product Line
• Feature Model Analysis
• Semi-Formal Blueprint
• Early Validation