Evaluating feature change impact on multi-product line configurations using partial information

Evolving large-scale, complex and highly variable systems is known to be a difficult task, where a single change can ripple through various parts of the system with potentially undesirable effects. In the case of product lines, and moreover multi-product lines, a change may affect only certain variants or certain combinations of features, making the evaluation of change effects more difficult.

In this paper, we present an approach for computing the impact of a feature change on the existing configurations of a multi-product line, using partial information regarding constraints between feature models. Our approach identifies the configurations that can no longer be derived in each individual feature model taking into account feature change impact propagation across feature models. We demonstrate our approach using an industrial problem and show that correct results can be obtained even with partial information. We also provide the tool we built for this purpose.