  The integration of cyber-physical systems (CPS) into everyday life raises the
critical necessity of ensuring their safety and reliability. An important step
in this direction is requirement mining, i.e. inferring formally specified
system properties from observed behaviors, in order to discover knowledge about
the system. Signal Temporal Logic (STL) offers a concise yet expressive
language for specifying requirements, particularly suited for CPS, where
behaviors are typically represented as time series data. This work addresses
the task of learning STL requirements from observed behaviors in a data-driven
manner, focusing on binary classification, i.e. on inferring properties of the
system which are able to discriminate between regular and anomalous behaviour,
and that can be used both as classifiers and as monitors of the compliance of
the CPS to desirable specifications. We present a novel framework that combines
Bayesian Optimization (BO) and Information Retrieval (IR) techniques to
simultaneously learn both the structure and the parameters of STL formulae,
without restrictions on the STL grammar. Specifically, we propose a framework
that leverages a dense vector database containing semantic-preserving
continuous representations of millions of formulae, queried for facilitating
the mining of requirements inside a BO loop. We demonstrate the effectiveness
of our approach in several signal classification applications, showing its
ability to extract interpretable insights from system executions and advance
the state-of-the-art in requirement mining for CPS.
