Abstract
Variability-aware computing is the efficient application of programs to different sets of inputs that exhibit some variability. One example is program analyses applied to Software Product Lines (SPLs). In this paper we present the design and development of a variability-aware version of the Soufflé Datalog engine. The engine can take facts annotated with Presence Conditions (PCs) as input, and compute the PCs of its inferred facts, eliminating facts that do not exist in any valid configuration. We evaluate our variability-aware Soufflé implementation on several fact sets annotated with PCs to measure the associated overhead in terms of processing time and database size.
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Shahin, R., Chechik, M. (2020). Variability-Aware Datalog. In: Komendantskaya, E., Liu, Y. (eds) Practical Aspects of Declarative Languages. PADL 2020. Lecture Notes in Computer Science(), vol 12007. Springer, Cham. https://doi.org/10.1007/978-3-030-39197-3_14
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DOI: https://doi.org/10.1007/978-3-030-39197-3_14
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