Tobias Heß
Chico Sundermann
Thomas Thüm
ABSTRACT
Binary decision diagrams (BDD) have been proposed for numerous product-line analyses. These analyses typically exploit properties unique to decision diagrams, such as negation in constant time and space. Furthermore, the existence of a BDD representing the configuration space of a product line removes the need to employ SAT or #SAT solvers for their analysis. Recent work has shown that the performance of state-of-the-art BDD libraries is significantly lower than previously reported and hypothesized. In this work, we provide an assessment of the state-of-the-art of BDD scalability in this domain and explain why previous results on the scalability of BDDs do not apply to more recent product-line instances.
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Index Terms
- On the scalability of building binary decision diagrams for current feature models
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