Abstract In a collaborative multi-user model-driven engineering context, it becomes important to track who changed what model part how and why. Operation-based versioning addresses this need by persisting a meaningful edit history which enables a single user to navigate through a model’s evolution over time, to analyze arbitrary previous model versions, or to trace the impact of an operation. However, to load a distinct prior version, it must be restored by reapplying all previous operations, which is time-consuming and, thus, interrupts a user’s workflow. Caching with a fixed distance between caches helps to overcome this problem to the cost of increasing memory requirements. Further, there is no caching approach supporting branches, merges, and possibly resolved conflicts. We propose two advanced caching strategies for operation-based versioning capable of the previously mentioned features: zonal and adaptive caching. Both strategies reduce the memory in use by not applying the same static distance between two caches across the whole edit history. Instead, the distance increases depending on a version’s age and its distance to a branch’s head. Both strategies aim to reduce the restoration time of arbitrary prior versions below a threshold to not interrupt a user’s flow of thought. Zonal caching employs predefined distances compatible with a broad range of model sizes. In contrast, adaptive caching derives the distances individually depending on the initial time to load the model on a user’s computer and the model’s size.We conducted controlled experiments with models of varying sizes and compared the time to restore model versions and the memory in use for no caching, caching with static distances, zonal, and adaptive strategies on different computers. The developed strategies decrease the time to restore a version remarkably while using less memory than static caching. Our results show that for all considered systems and models individual adaptive caching reduces memory usage even further compared to zone-based caching while still satisfying application responsiveness requirements.

Feature models are commonly used to specify valid configurations of a product line. In industry, feature models are often complex due to numerous features and constraints. Thus, a multitude of automated analyses have been proposed. Many of those rely on computing the number of valid configurations, which typically depends on solving a # SAT problem, a computationally expensive operation. Even worse, most counting-based analyses require evaluation for multiple features or partial configurations resulting in numerous # SAT computations on the same feature model. Instead of repetitive computations on highly similar formulas, we aim to improve the performance by reusing knowledge between these computations. In this work, we are the first to propose reusing d-DNNFs for performing repetitive counting queries on features and partial configurations. In our experiments, reusing d-DNNFs saved up-to \(\sim\) 99.98% compared to repetitive invocations of # SAT solvers even when including compilation times. Overall, our tool ddnnife combined with the d-DNNF compiler d4 appears to be the most promising option when dealing with many repetitive feature-model counting queries.