When to Extract Features: Towards a Recommender System

In practice, many organizations rely on cloning to implement customer-specific variants of a system. While this approach can have several disadvantages, organizations fear to extract reusable features later on, due to the corresponding efforts and risks. A particularly challenging and poorly supported task is to decide which features to extract. To tackle this problem, we aim to develop a recommender system that proposes suitable features based on automated analyses of the cloned legacy systems. In this paper, we sketch this recommender and its empirically derived metrics, which comprise cohesion, impact, and costs of features as well as the users' previous decisions. Overall, we will facilitate the adoption of systematic reuse based on an integrated platform.