Johannes Sigel

Abstract

Software-intensive systems emerge in a multitude of variations to meet diverse customer requirements. To develop such variant-rich software systems, software product line (SPL) Engineering has emerged as a key strategy for managing the variability. However, the adoption of SPLs is highly complex due to the diversity of feature model formats and specifications, and the complexity of implementing variability. Leveraging the capabilities of powerful large language models (LLMs) can facilitate the adoption of SPLs. Nonetheless, these LLMs often lack knowledge of the various specifications of potential feature models as well as efficient implementation of different variability mechanisms. To address these challenges, we propose a novel method based on retrieval-augmented generation. This method generates reusable artefacts and a corresponding feature mapping based on a given feature model, thereby aiding system engineers in adopting an SPL.