We propose to use algorithms for learning deterministic finite automata (DFA), such
as Angluin’s L
∗ algorithm, for learning a DFA that describes the possible scenarios under
which a given program error occurs. The alphabet of this automaton is given by the user
(for instance, a subset of the function call sites or branches), and hence the automaton
describes a user-defined abstraction of those scenarios. More generally, the same technique
can be used for visualising the behavior of a program or parts thereof. It can also be used
for visually comparing different versions of a program (by presenting an automaton for the
behavior in the symmetric difference between them), and for assisting in merging several
development branches. We present experiments that demonstrate the power of an abstract
visual representation of errors and of program segments, accessible via the project’s web
page. In addition, our experiments in this paper demonstrate that such automata can
be learned efficiently over real-world programs. We also present lazy learning, which is a
method for reducing the number of membership queries while using L∗, and demonstrate its effectiveness on standard benchmarks
