  Computer algorithms are written with the intent that when run they perform a
useful function. Typically any information obtained is unknown until the
algorithm is run. However, if the behavior of an algorithm can be fully
described by precomputing just once how this algorithm will respond when
executed on any input, this precomputed result provides a complete
specification for all solutions in the problem domain. We apply this idea to a
previous anomaly detection algorithm, and in doing so transform it from one
that merely detects individual anomalies when asked to discover potentially
anomalous values, into an algorithm also capable of generating a complete
specification for those values it would deem to be anomalous. This
specification is derived by examining no more than a small training data, can
be obtained in very small constant time, and is inherently far more useful than
results obtained by repeated execution of this tool. For example, armed with
such a specification one can ask how close an anomaly is to being deemed
normal, and can validate this answer not by exhaustively testing the algorithm
but by examining if the specification so generated is indeed correct. This
powerful idea can be applied to any algorithm whose runtime behavior can be
recovered from its construction and so has wide applicability.
