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Today, software verification tools have reached the maturity to be used for large scale programs. Different tools perform differently well on varying code. A software developer is hence faced with the problem of choosing a tool appropriate for her program at hand. A ranking of tools on programs could facilitate the choice. Such rankings can, however, so far only be obtained by running all considered tools on the program. In this paper, we present a machine learning approach to predictingdoi:10.1145/3121257.3121262 dblp:conf/sigsoft/CzechHJW17 fatcat:sxx7gdxg75g2pnou4qqa6si75m