Interactive search-based Product Line Architecture design

Abstract

Software Product Line (SPL) is an approach derived from other engineering fields that use reuse techniques for a family of products in a given domain. An essential artifact of SPL is the Product Line Architecture (PLA), which identifies elements characterized by variation points, variability, and variants. The PLA aims to anticipate design decisions to obtain features such as reusability and modularity. Nevertheless, getting a reusable and modular PLA and following pre-defined standards can be a complex task involving several conflicting objectives. In this sense, PLA can be formulated as a multiobjective optimization problem. This research presents an approach that helps DMs (Decision Makers) to interactively optimize the PLAs through several strategies such as interactive optimization and Machine Learning (ML) algorithms. The interactive multiobjective optimization approach for PLA design (iMOA4PLA) uses specific metrics for the PLA optimization problem, implemented through the OPLA-Tool v2.0. In this approach, the architect assumes the role of DM during the search process, guiding the evolution of PLAs through various strategies proposed in previous works. Two quantitative and one qualitative experiments were performed to evaluate the iMOA4PLA. The results showed that this approach can assist the PLA optimization process by meeting more than 90% of DM preferences. The scientific contribution of this work lies in providing an approach for the PLA design and evaluation that leverages the benefits of machine learning algorithms and can serve as a basis for different SE contexts.