Formal Verification (FV) and Machine Learning (ML) can seem incompatible due
to their opposite mathematical foundations and their use in real-life problems:
FV mostly relies on discrete mathematics and aims at ensuring correctness; ML
often relies on probabilistic models and consists of learning patterns from
training data. In this paper, we postulate that they are complementary in
practice, and explore how ML helps FV in its classical approaches: static
analysis, model-checking, theorem-proving, and SAT solving. We draw a landscape
of the current practice and catalog some of the most prominent uses of ML
inside FV tools, thus offering a new perspective on FV techniques that can help
researchers and practitioners to better locate the possible synergies. We
discuss lessons learned from our work, point to possible improvements and offer
visions for the future of the domain in the light of the science of software
and systems modeling.Comment: 13 pages, no figures, 3 table