Early-Stage Product Line Validation Using LLMs: A Study on Semi-Formal Blueprint Analysis
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.
Cite
@article{arxiv.2604.20523,
title = {Early-Stage Product Line Validation Using LLMs: A Study on Semi-Formal Blueprint Analysis},
author = {Viet-Man Le and Thi Ngoc Trang Tran and Sebastian Lubos and Alexander Felfernig and Damian Garber},
journal= {arXiv preprint arXiv:2604.20523},
year = {2026}
}
Comments
The 41st ACM/SIGAPP Symposium on Applied Computing (SAC '26), March 23--27, 2026, Thessaloniki, Greece DOI: 10.1145/3748522.3779903