Biblio

Many applications require not only representing variability in software and data, but also computing with it. To do so efficiently requires variational data structures that make the variability explicit in the underlying data and the operations used to manipulate it. Variational data structures have been developed ad hoc for many applications, but there is little general understanding of how to design them or what tradeoffs exist among them. In this paper, we strive for a more systematic exploration and analysis of a variational data structure. We want to know how different design decisions affect the performance and scalability of a variational data structure, and what properties of the underlying data and operation sequences need to be considered. Specifically, we study several alternative designs of a variational stack, a data structure that supports efficiently representing and computing with multiple variants of a plain stack, and that is a common building block in many algorithms. The different variational stacks are presented as a small product line organized by three design decisions. We analyze how these design decisions affect the performance of a variational stack with different usage profiles. Finally, we evaluate how these design decisions affect the performance of the variational stack in a real-world scenario: in the interpreter VarexJ when executing real software containing variability.