Variability-aware Datalog

TL;DR

This paper introduces a variability-aware extension of the Soufflé Datalog engine that efficiently handles facts with presence conditions, enabling analysis across different configurations with minimal overhead.

Contribution

It presents the design and implementation of a variability-aware Soufflé engine capable of processing annotated facts and computing presence conditions for inferred facts.

Findings

The variability-aware Soufflé engine successfully processes annotated facts.

Overhead in processing time and database size is measured and analyzed.

The approach improves analysis efficiency for software product lines.

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 Souffl\'{e} 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 Souffl\'{e} implementation on several fact sets annotated with PCs to measure the associated overhead in terms of processing time and database size.