We present a supervised, interactive learning technique that
infers control structures of computer programs from user-demonstrated
traces. A two-stage process is applied: First, a minimal deterministic 
finite automaton (DFA) M labeled by the instructions of the program
is learned from a set of example traces and membership queries to the
user. It accepts all prefixes of traces of the target program. The number
of queries is bounded by O(k |M|), with k being the total number of instructions
in the initial example traces. In the second step we parse this
automaton into a high-level programming language in O(|M|^2) steps,
replacing jumps by conditional control structures.