Fully Automated Software Product Line Evolution With Diverse Artifacts
Jakub Perdek, Valentino Vranić · IEEE Access · 2025
Existing approaches in software product lines usually neglect knowledge modeling and simulation of the interaction between features capable of bringing dynamism and automation. Consequently, these solutions miss opportunities to resolve associated and emerging problems, including defect detection or quality assurance, which can be solved by effectively extracting and utilizing knowledge from data based on the differences between variants. We bring capabilities to seize them in introducing a fully automated and minimalistic approach to software product line evolution that strictly focuses on handling variability at low-level code fragments. It incorporates the autonomous modeling of emerging knowledge across preconfigured simulations. Specifically, fully automated knowledge-driven software product line evolution provides various views on an existing software product line, its variants, and their evolution through semantic and structural information accompanied by the time and order of the performed changes. We initially developed our approach for software product line evolution, followed by its successful application to the evolution of fractal scripts. We present it accordingly. Fractal products are much simpler because an application state does not span out of recursive behavior, making it manageable within the drawing. Each change is propagated into repetitive phases, causing the visual performance of the implemented feature to be infinitely detailed. Even minor changes in low code fragments tend to manifest as user-visible features owing to recursive behavior, allowing one to massively introduce new features and/or configure existing features and manage variability observable as infinitely detailed shapes. Future applications propose automated observation of more comprehensive in-code representation of feature trees.