Machine Learning Product Line Engineering: A Systematic Reuse Framework

Machine Learning (ML) is increasingly applied across various domains, addressing tasks such as predictive analytics, anomaly detection, and decision-making. Many of these applications share similar underlying tasks, offering potential for systematic reuse. However, existing reuse in ML is often fragmented, small-scale, and ad hoc, focusing on isolated components such as pretrained models or datasets without a cohesive framework. Product Line Engineering (PLE) is a well-established approach for achieving large-scale systematic reuse in traditional engineering. It enables efficient management of core assets like requirements, models, and code across product families. However, traditional PLE is not designed to accommodate ML-specific assets—such as datasets, feature pipelines, and hyperparameters—and is not aligned with the iterative, data-driven workflows of ML systems. To address this gap, we propose Machine Learning Product Line Engineering (ML PLE), a framework that adapts PLE principles for ML systems. In contrast to conventional ML reuse methods such as transfer learning or fine-tuning, our framework introduces a systematic, variability-aware reuse approach that spans the entir