Predicting rankings of software verification tools

Mike Czech, Eyke Hüllermeier, Marie-Christine Jakobs, Heike Wehrheim
2017 Proceedings of the 3rd ACM SIGSOFT International Workshop on Software Analytics - SWAN 2017  
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 predicting
more » ... s of tools on programs. The method builds upon so-called label ranking algorithms, which we complement with appropriate kernels providing a similarity measure for programs. Our kernels employ a graph representation for software source code that mixes elements of control flow and program dependence graphs with abstract syntax trees. Using data sets from the software verification competition SV-COMP, we demonstrate our rank prediction technique to generalize well and achieve a rather high predictive accuracy (rank correlation > 0.6).
doi:10.1145/3121257.3121262 dblp:conf/sigsoft/CzechHJW17 fatcat:sxx7gdxg75g2pnou4qqa6si75m