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Formal Verification (FV) and Machine Learning (ML) can seem incompatible dueto their opposite mathematical foundations and their use in real-life problems:FV mostly relies on discrete mathematics and aims at ensuring correctness; MLoften relies on probabilistic models and consists of learning patterns fromtraining data. In this paper, we postulate that they are complementary inpractice, and explore how ML helps FV in its classical approaches: staticanalysis, model-checking, theorem-proving, and SAT solving. We draw a landscapeof the current practice and catalog some of the most prominent uses of MLinside FV tools, thus offering a new perspective on FV techniques that can helpresearchers and practitioners to better locate the possible synergies. Wediscuss lessons learned from our work, point to possible improvements and offervisions for the future of the domain in the light of the science of softwareand systems modeling.

ML + FV = $\heartsuit$? A Survey on the Application of Machine Learning to Formal Verification