Scalable Analysis of Variable Software Jörg

The advent of variability management and generator technol-ogy enables users to derive individual variants from a variable code base based on a selection of desired configuration op-tions. This approach gives rise to the generation of possibly billions of variants that, however, cannot be efficiently ana-lyzed for errors with classic analysis techniques. To address this issue, researchers and practitioners usually apply sam-pling heuristics. While sampling reduces the analysis effort significantly, the information obtained is necessarily incom-plete and it is unknown whether sampling heuristics scale to billions of variants. Recently, researchers have begun to develop variability-aware analyses that analyze the variable code base directly exploiting the similarities among individ-ual variants to reduce analysis effort. However, while being promising, so far, variability-aware analyses have been ap-plied mostly only to small academic systems. To learn about the mutual strengths and weaknesses of variability-aware and sampling-based analyses of software systems, we compared the two strategies by means of two concrete analysis imple-mentations (type checking and liveness analysis), applied them to three subject systems: Busybox, the x86 Linux ker-nel, and OpenSSL. Our key finding is that variability-aware analysis outperforms most sampling heuristics with respect to analysis time while preserving completeness