Linear temporal logic (LTL) is a specification language for finite sequences (called traces)
widely used in program verification, motion planning in robotics, process mining, and many …

In runtime verification, manually formalizing a specification for monitoring system executions
is a tedious and error-prone process. To address this issue, we consider the problem of …

Inferring a LTL f formula from a set of example traces, also known as passive learning, is a
challenging task for model-based techniques. Despite the combinatorial nature of the …

Linear temporal logic (LTL) is widely used in industrial verification. LTL formulae can be
learned from traces. Scaling LTL formula learning is an open problem. We implement the …

We aim to determine which temporal instance queries can be uniquely characterised by a
(polynomial-size) set of positive and negative temporal data examples. We start by …

Virtually all verification and synthesis techniques assume that formal specifications are
readily available, functionally correct, and fully match the engineer's understanding of the …

The problem of passive learning of linear temporal logic formulae consists in finding the best
explanation for how two sets of execution traces differ, in the form of the shortest formula that …

We address the problem of learning temporal properties from the branching-time behavior of
systems. Existing research in this field has mostly focused on learning linear temporal …

We consider the problem of learning temporal logic formulas from examples of system
behavior. Learning temporal properties has crystallized as an effective mean to explain …

In reverse engineering of database queries, we aim to construct a query from a given set of
answers and non-answers; it can then be used to explore the data further or as an …