Breaking Weighted Model Counting Solvers Using EXTREMEgen
Generating WMC instances for fuzzing
B.P. Snelten (TU Delft - Electrical Engineering, Mathematics and Computer Science)
A.L.D. Latour – Mentor (TU Delft - Algorithmics)
M. Skrodzki – Graduation committee member (TU Delft - Computer Graphics and Visualisation)
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Abstract
Weighted model counting (WMC) solvers play a key role in Bayesian inference applications, used for medical diagnosis [17] [16] and risk assessment [14]. Ongoing efforts to improve WMC solver developers aim to develop a fuzzer to identify bugs. This research is aimed at enhancing the quality of this fuzzer by developing an additional method to generate WMC instances, EXTREMEgen [21]. The functionality of this new approach relies on generating practical instances from Bayesian networks and breaking the solvers using extreme weights. Our empirical experiments show that EXTREMEgen exposes bugs in state-ofthe-art WMC solvers. The generated instances are solved fast enough to be usable in fuzzing. However, generation speed needs to be optimized to become practical for fuzzing. When optimized, EXTREMEgen could become the first generator of WMC instances specifically designed for fuzzing.