Abstract
As a Software Product Line (SPL) evolves variability specifications in problem space and variability realizations in solution space erode over time and impact productivity during development. On the one hand, the variability model tends to be incomplete and inconsistent with the core assets; on the other hand, the core assets become overly complex, which make them difficult to understand and maintain. In this paper, we present the RECoVar framework towards reverse engineering of SPL variability. The framework includes two approaches: a) code-based variability model extraction; and b) complex feature correlation mining. These two approaches help to extract various variability information, so that variability specifications and realizations can be maintained in an efficient way.