Abstract
Anti-random testing has proved useful in a series of empirical evaluations. The basic premise of anti-random testing is to chose new test vectors that are as far away from existing test inputs as possible. The distance measure is either Hamming distance or Cartesian distance. Unfortunately, this method essentially requires enumeration 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 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 of applying anti-random vectors for behavioral model verification using branch coverage as the testing criterion.
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Chen, T., Bai, A., Hajjar, A. et al. Fast Anti-Random (FAR) Test Generation to Improve the Quality of Behavioral Model Verification. Journal of Electronic Testing 18, 583–594 (2002). https://doi.org/10.1023/A:1020844805564
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DOI: https://doi.org/10.1023/A:1020844805564