Distance-Integrated Combinatorial Testing

Abstract

Abstract-This paper proposes a novel approach to combinatorial test generation, which achieves an increase of not only the number of new combinations but also the distance between test cases. We applied our distance-integrated approach to a stateof-the-art greedy algorithm for traditional combinatorial test generation by using two distance metrics, Hamming distance, and a modified chi-square distance. Experimental results using numerous benchmark models show that combinatorial test suites generated by our approach using both distance metrics can improve interaction coverage for higher interaction strengths with low computational overhead.

Optimal Controlled Random Tests

“…Unfortunately the basic antirandom method essentially requires enumeration of the input space and computation of distances for each potential input vector [14]. Therefore many modification of antirandom tests have been proposed [15][16][17][18][19][20][21][22][23][24]. But even for improved version of the method, very often computations become too expensive for real dimension N of the test vectors [25].…”