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Modern software systems are increasingly designed to be highly configurable,which increases flexibility but can make programs harder to develop, test, andanalyze, e.g., how configuration options are set to reach certain locations,what characterizes the configuration space of an interesting or buggy programbehavior? We introduce GenTree, a new dynamic analysis that automaticallylearns a program's interactions - logical formulae that describe howconfiguration option settings map to code coverage. GenTree uses an iterativerefinement approach that runs the program under a small sample ofconfigurations to obtain coverage data; uses a custom classifying algorithm onthese data to build decision trees representing interaction candidates; andthen analyzes the trees to generate new configurations to further refine thetrees and interactions in the next iteration. Our experiments on 17configurable systems spanning 4 languages show that GenTree efficiently findsprecise interactions using a tiny fraction of the configuration space.

GenTree: Using Decision Trees to Learn Interactions for Configurable Software