Partial Covering Arrays: Algorithms and Asymptotics (1605.02131v1)

Abstract: A covering array $\mathsf{CA}(N;t,k,v)$ is an $N\times k$ array with entries in ${1, 2, \ldots , v}$, for which every $N\times t$ subarray contains each $t$-tuple of ${1, 2, \ldots , v}^t$ among its rows. Covering arrays find application in interaction testing, including software and hardware testing, advanced materials development, and biological systems. A central question is to determine or bound $\mathsf{CAN}(t,k,v)$, the minimum number $N$ of rows of a $\mathsf{CA}(N;t,k,v)$. The well known bound $\mathsf{CAN}(t,k,v)=O((t-1)v^t\log k)$ is not too far from being asymptotically optimal. Sensible relaxations of the covering requirement arise when (1) the set ${1, 2, \ldots , v}^t$ need only be contained among the rows of at least $(1-\epsilon)\binom{k}{t}$ of the $N\times t$ subarrays and (2) the rows of every $N\times t$ subarray need only contain a (large) subset of ${1, 2, \ldots , v}^t$. In this paper, using probabilistic methods, significant improvements on the covering array upper bound are established for both relaxations, and for the conjunction of the two. In each case, a randomized algorithm constructs such arrays in expected polynomial time.