One of the basic activities in domain-specific software reuse is product derivation, which is deriving individual software products from the reusable software artifacts produced beforehand in the domain. The efficiency of product derivation decides the benefits of software reuse. Among all of the factors affecting the efficiency of product derivation, derivation being carried out manually is a major aspect with negative impacts that reduces the benefits of software reuse as a result. To improve the efficiency of product derivation, some approaches have been proposed to automate the derivation activity. A widely adopted idea in the approaches is automating the derivation activity based on feature models. In the approaches sharing the idea above, the implementation methods differ widely from one to another. To provide better support for feature model-based automated product derivation, this paper proposes a framework for classifying and analyzing these approaches. The paper also points out the problems in the existing researches and the possible solutions to the problems.