The advent of variability management and generator technology enables users to derive individual system variants from a configurable code base by selecting desired configuration options. This approach gives rise to the generation of possibly billions of variants, which, however, cannot be efficiently analyzed for bugs and other properties with classic analysis techniques. To address this issue, researchers and practitioners have developed sampling heuristics and, recently, variability-aware analysis techniques. While sampling reduces the analysis effort significantly, the information obtained is necessarily incomplete, and it is unknown whether state-of-the-art sampling techniques scale to billions of variants. Variability-aware analysis techniques process the configurable code base directly, exploiting similarities among individual variants with the goal of reducing analysis effort. However, while being promising, so far, variability-aware analysis techniques have been applied mostly only to small academic examples. To learn about the mutual strengths and weaknesses of variability-aware and sample-based static-analysis techniques, we compared the two by means of seven concrete control-flow and data-flow analyses, applied to five real-world subject systems: B usybox , O pen SSL, SQL ite , the x86 L inux kernel, and u C libc . In particular, we compare the efficiency (analysis execution time) of the static analyses and their effectiveness (potential bugs found). Overall, we found that variability-aware analysis outperforms most sample-based static-analysis techniques with respect to efficiency and effectiveness. For example, checking all variants of O pen SSL with a variability-aware static analysis is faster than checking even only two variants with an analysis that does not exploit similarities among variants.

Today, many software systems are configurable with conditional compilation. Just like any software system, configurable systems need to be refactored in their evolution, but their inherent variability induces an additional dimension of complexity that is not addressed well by current academic and industrial refactoring engines. To improve the state of the art, we propose a variability-aware refactoring approach that relies on a canonical variability representation and recent work on variability-aware analysis. The goal is to preserve the behavior of all variants of a configurable system, without compromising general applicability and scalability. To demonstrate practicality, we developed Morpheus, a sound, variability-aware refactoring engine for C code with preprocessor directives. We applied Morpheus to three substantial real-world systems (Busybox, OpenSSL, and SQLite) showing that it scales reasonably well, despite of its heavy reliance on satisfiability solvers. By extending a standard approach of testing refactoring engines with support for variability, we provide evidence for the correctness of the refactorings implemented.