## 9 Testing and Validation of Product Variants
### ML PLE Process  
As stated before, integrating ML PLE requires a redefined lifecycle process to address the dynamic, iterative, and data-driven nature of ML systems. This lifecycle incorporates traditional PLE principles while adapting them to ML-specific workflows and assets. Figure 7 shows the ML PLE lifecycle process with the enhanced Domain Engineering and Application Engineering processes.  
The ML Domain Engineering process results in a ML Core Asset base with the ML Core assets that we have described before. Each of these assets can be at different readiness level. The higher the readiness level, the less additional effort is needed when these are reused in the application engineering process to implement a machine learning problem.  
To align the ML PLE process with established PLE practices, we adopt the conventional structure used in both domain and application engineering: product management, requirements engineering, design, implementation, and testing. Hereby, Tables 4 and 5 present a structured mapping of ML-specific activities to each of these phases, capturing the adaptations needed to address the distinct characteristics of ML development, such as data-driven requirements, reusable model pipelines, configuration variability, and iterative feedback. This mapping enables the integration of machine learning into the broader PLE process, promoting consistency, traceability, and systematic reuse across product lines.  
The ML domain engineering process begins with scoping, where the product line's scope is defined by identifying commonalities and variability in datasets, models, pipelines, and tasks. Reusable assets such as datasets, feature extraction pipelines, model architectures, training workflows, and hyperparameters are then identified, with an emphasis on configurability. Variability modeling captures differences and options for datasets, preprocessing techniques, model configurations, and pipelines. Core asset development focuses on creating modular and reusable ML assets, including labeled datasets, feature engineering workflows, model templates, and automated pipelines. Traceability links are established between requirements, core assets, and variability models to ensure consistency and systematic updates. Testing and validation confirm the quality and reusability of core assets, while comprehensive documentation ensures traceability and reproducibility. Finally, core assets and variability models are continuously maintained and updated based on feedback and evolving requirements.  
Figure 7: ML PLE Lifecycle Process.  
The ML application engineering process involves configuring and customizing reusable core assets to derive specific ML-enabled products. Datasets, models, and pipelines are selected and adapted to meet the target application's requirements, guided by variability models. Application-specific training and fine-tuning align the models with task-specific goals, such as optimizing for accuracy or latency. Configured pipelines are validated to ensure performance and reliability, followed by deployment into production. Monitoring pipelines are implemented to track performance, detect drift, and enable retraining as needed. Feedback from deployment informs updates to core assets and variability models, enabling continuous improvement. This iterative process ensures efficient derivation of ML-enabled products while leveraging the systematic reuse established during domain engineering.