Understanding Variable Code: Reducing the Complexity by Integrating Variability Information

Software product lines often use preprocessor statements as a basis for representing variability, which makes understanding the artifacts rather complex. An approach that has been proposed in the past to improve the understanding of code with preprocessor statements is formal concept analysis. This approach has been applied to a number of causes in reengineering. However, the lattices constructed by this approach can become rather large and complex. Hence, any approach that helps to reduce them can be beneficial to understanding the preprocessor-dependencies contained in the code. Here, we show how consistency analysis both within code variability and between code and a variability model can be used to reduce the complexity of a lattice, supporting the analysis of product-line code. We apply our approach to Linux, one of the largest open-source product lines, and analyze both multiple versions and different architectures. We show that our approach typically leads to reductions of the concept lattice and identify situations in which the savings can be rather significant. This leads to a reduction of any efforts for followup analysis or reverse engineering.