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Lecture Notes in Computer Science
We study empirical metrics for software source code, which can predict the performance of verification tools on specific types of software. Our metrics comprise variable usage patterns, loop patterns, as well as indicators of control-flow complexity and are extracted by simple data-flow analyses. We demonstrate that our metrics are powerful enough to devise a machine-learning based portfolio solver for software verification. We show that this portfolio solver would be the (hypothetical) overalldoi:10.1007/978-3-319-21690-4_39 fatcat:y7dktz6gfreq5dwhlck3ho3wwi