Clone-and-Own software product derivation based on developer preferences and cost estimation

Abstract

Clone-and-own is a common reuse practice that is widely adopted for evolving a family of software systems. However, this practice loses its effectiveness if not supported with valuable indicators that guide the derivation of new products. In this paper, we propose an approach to support the derivation of new product variants based on clone-and-own, by providing the possible scenarios in terms of operations to perform to accomplish the derivation. We generate a constraints system prior to a product derivation, to facilitate the software engineer selection of the suitable scenario and operations based on his preferences. In addition, we propose a cost estimation for each operation and respectively for each scenario, thus, a software engineer can rely on it as an additional parameter to achieve the derivation. The proposed scenarios and cost estimation are based on indicators retrieved after an automated identification of the mappings between the features implemented by the family of software products and the assets in which they are implemented. We preliminarily validate our approach on a case study where results show that the provided support can considerably reduce the amount of time and efforts that can be required to achieve a product derivation.