## 6 Qualitative Evaluation
### Case Study Evaluation  
In the industrial case study involving a family of space systems, the ML PLE framework was applied to guide the development of machine learning components across multiple satellite missions. These systems, characterized by their data-intensive and mission-critical nature, presented a suitable context for exploring the applicability of systematic reuse in machine learning development. The framework supported the reuse of core assets such as preprocessing pipelines, data transformation workflows, and model templates, while allowing for their adaptation to the specific operational requirements and constraints of each mission. The structured approach provided by ML PLE--particularly in terms of asset modularization, variability management, and lifecycle traceability--enabled a more consistent, transparent, and maintainable development process. Teams were able to more easily identify and reuse recurring components, reduce redundancy in the design of data pipelines, and maintain clearer mappings between reusable assets and mission-specific configurations. To assess the feasibility of applying PLE principles, a feasibility analysis was conducted for software artefacts, which evaluated the potential for systematic reuse, cost savings, and scalability in the context of software components. Given the similarities in reuse objectives, the feasibility analysis for machine learning artefacts was extrapolated from the software PLE analysis, adapting it to account for the data-centric and iterative nature of ML workflows. This extrapolation considered factors such as data pipeline modularity, model adaptability, and the complexity of ML-specific variability management. To compare the development approaches, we consider the general cost model for establishing a software product line as defined by [PERSON] et al. (2015). The total cost C for developing a set of n products p is expressed in Equation (1):  
\[C=C_{Org}+C_{Cab}+\sum_{i=1}^{n}\bigl{(}C_{Unique}(p_{i})+C_{Reuse}(p_{i}) \bigr{)} \tag{1}\]  
These parameters represent the cost of adapting the software reuse approach for the organization (\(C_{Org}\)), the cost to define the asset base (\(C_{Cab}\)), the cost for developing unique products (\(C_{Unique}\)), and the cost of reusing core assets (\(C_{Reuse}\)). Different formulas for different strategies based on Equation (1). We illustrate the comparison in Figure 8, which contrasts three approaches to system development: single-system development, conventional PLE, and ML PLE. We distinguish between three types of initial investment: the investment required to move from single-system development to conventional PLE (Inv-PL), the investment required to move directly from single-system development to ML PLE (Inv-ML PLE), and the incremental investment needed to extend a conventional PLE approach to an ML PLE strategy (Inv-PL to ML PLE). Correspondingly, we identify three types of return on investment (ROI): the ROI of adopting conventional PLE over single-system development, the ROI of adopting ML PLE directly over single-system development, and the ROI gained from extending conventional PLE with machine learning capabilities. Each of these approaches also presents a different breakeven point, depending on the number of products and the extent of reuse achieved. As shown in Figure 8, ML PLE requires the highest upfront investment due to the complexity of managing data-centric, iterative ML workflows. However, it also offers the highest long-term returns when reuse of ML assets is frequent and critical. Conventional PLE sits between ML PLE and single-system development in both investment and benefit, offering strong reuse potential without the added complexity of ML. Overall, ML PLE extends the advantages of conventional PLE by incorporating reusable ML-specific components, making it particularly valuable in data-intensive and ML-driven domains.