Abstract
Anti-random testing has proved useful in a series of empirical evaluations. The basic premise of anti-random testing is to choose new test vectors that are as far away from existing test inputs as possible. The distance measure is the Hamming or Cartesian distance. Unfortunately, this method essentially requires emuneration of the input space and computation of each input vector when used on an arbitrary set of existing test data. This prevents scale-up to a large test sets and/or long input vectors. We present and empirically evaluate a technique to generate anti-random vectors that is computationally feasible for large input vectors and long sequences of tests. We also show how this fast anti-random test generation (FAR) can consider retained state (i.e. effects of subsequent inputs on each other). We evaluate effectiveness using branch coverage as the testing criterion.