Feature Selection Optimization in Software Product Lines

Feature modeling is a common approach for configuring and capturing commonalities and variations among different Software Product Lines (SPL) products. This process is carried out by a set of SPL design teams, each working on a different configuration of the desired product. The integration of these configurations leads to inconsistencies in the final product design. The typical solution involves extensive deliberation and unnecessary resource usage, which makes SPL inconsistency resolution an expensive and unoptimized process. We present the first comprehensive evaluation of swarm intelligence (using Particle Swarm Optimization) to the problem of resolving inconsistencies in a configured integrated SPL product. We call it <inline-formula> <tex-math notation="LaTeX">${o}$ </tex-math></inline-formula>-SPLIT (<inline-formula> <tex-math notation="LaTeX">${o}$ </tex-math></inline-formula>ptimization-based Software Product LIne Tool) and validate <inline-formula> <tex-math notation="LaTeX">${o}$ </tex-math></inline-formula>-SPLIT with standard ERP, SPLOT (Software Product Lines Online Tools), and BeTTy (BEnchmarking and TesTing on the analYsis) product configurations along with diverse feature set sizes. The results show that Particle Swarm Optimization can successfully optimize SPL product configurations. Finally, we implement <inline-formula> <tex-math notation="LaTeX">${o}$ </tex-math></inline-formula>-SPLIT as a decision-support tool in a real, local SPL setting and acquire subjective feedback from SPL designers which shows that the teams are convinced of the usability and high-level decision support provided by <inline-formula> <tex-math notation="LaTeX">${o}$ </tex-math></inline-formula>-SPLIT.