Heuristic and exact algorithms for product configuration in software product lines

The Software Product Line (SPL) configuration field is an active area of research and has attracted both practitioners and researchers attention in the last years. A key part of an SPL configuration is a feature model that represents features and their dependencies (i.e., SPL configuration rules). This model can be extended by adding Non-Functional Properties (NFPs) as feature attributes resulting in Extended Feature Models (EFMs). Configuring products from an EFM requires considering the configuration rules of the model and satisfying the product functional and non-functional requirements. Although the configuration of a product arising from EFMs may reduce the space of valid configurations, selecting the most appropriate set of features is still an overwhelming task due to many factors including technical limitations and diversity of contexts. Consequently, configuring large and complex SPLs by using configurators is often beyond the users' capabilities of identifying valid combinations of features that match their (non-functional) requirements. To overcome this limitation, several approaches have modeled the product configuration task as a combinatorial optimization problem and proposed constraint programming algorithms to automatically derive a configuration. Although these approaches do not require any user intervention to guarantee the optimality of the generated configuration, due to the NP-hard computational complexity of finding an optimal variant, exact approaches have inefficient exponential time. Thus, to improve scalability and performance issues, we introduced the adoption of a greedy heuristic algorithm and a biased random-key genetic algorithm (BRKGA). Our experiment results show that our proposed heuristics found optimal solutions for all instances where those are known. For the instances where optimal solutions are not known, the greedy heuristic outperformed the best solution obtained by a one-hour run of the exact algorithm by up to 67.89%. Although the BRKGA heuristic slightly outperformed the greedy heuristic, it has shown larger running times (especially on the largest instances). Therefore, to ensure a good user experience and enable a very fast configuration task, we extended a state-of-the-art configurator with the proposed greedy heuristic approach.