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The Product Line Architecture (PLA) plays a central role at the products development from a Software Product Line (SPL). PLA design is a people-intensive and non-trivial task. So, PLA design can be considered a hard problem which could be formulated as an optimization problem with many factors to be solved by search algorithms. In this sense, the approach named MOA4PLA (Multi-Objective Approach for Product-Line Architecture Design) was proposed to automatically identify the best alternatives for a PLA design. This approach originally included metrics to evaluate basic design principles, feature modularization, design elegance and SPL extensibility. However, there are other relevant properties for PLA design. For this reason, the evaluation model of MOA4PLA was extended with metrics to measure the level of similarity and adaptability of the PLA. The objective of this work is to investigate the possible correlation between the metrics related to similarity and variability in order to decrease the number of functions to be optimized. To do this, three experiments were carried out. Empirical results allow to learn some lessons regarding to these metrics in the referred context.

Correlation between Similarity and Variability Metrics in Search-based Product Line Architecture: Experimental Study and Lessons Learned