Bridging Feature Models and Users: LLM-Generated Low-Code Configurators for Software Product Lines
TL;DR
This work draws on principles from Low-Code Development (LCD) and introduces an LLM-driven approach for automatically generating interactive Web-based configurators directly from feature models, to simplify the development of configurators and support feature selection itself through low-code techniques that make variability accessible to non-technical users.
Abstract
Software Product Lines (SPLs) promise substantial efficiency gains by systematically managing variability across families of related software systems. Realizing these benefits requires a configuration process, performed at compile time, runtime, or both, in which users select and configure features to derive a concrete product instance from an SPL. However, bridging the gap between such feature models and usable product configurations remains a persistent challenge. Users must navigate large variability models, interpret technical terminology, and manually satisfy complex dependency constraints, resulting in significant cognitive load for domain experts and increased development effort. To address these challenges, we draw on principles from Low-Code Development (LCD) and introduce an LLM-driven approach for automatically generating interactive Web-based configurators directly from feature models. Our goal is not only to simplify the development of configurators, but also to support feature selection itself through low-code techniques that make variability accessible to non-technical users. Rather than producing a single interface, our approach synthesizes multiple configuration experiences, including step-by-step wizards, visual dashboards, and profile-based guidance, tailored to different user preferences and mental models. All generated configurators preserve the semantics of the underlying feature model by enforcing variability constraints, while reducing complexity through contextual explanations, intuitive labeling, and guided interactions. We demonstrate the benefits of our approach through exploratory experiments across diverse proxy scenarios and expert interviews with specialists in SPLs and Human-Computer Interaction (HCI).