  Combinatorial interaction testing is an efficient software testing strategy.
If all interactions among test parameters or factors needed to be covered, the
size of a required test suite would be prohibitively large. In contrast, this
strategy only requires covering $t$-wise interactions where $t$ is typically
very small. As a result, it becomes possible to significantly reduce test suite
size. Locating arrays aim to enhance the ability of combinatorial interaction
testing. In particular, $(\overline{1}, t)$-locating arrays can not only
execute all $t$-way interactions but also identify, if any, which of the
interactions causes a failure. In spite of this useful property, there is only
limited research either on how to generate locating arrays or on their minimum
sizes. In this paper, we propose an approach to generating minimum locating
arrays. In the approach, the problem of finding a locating array consisting of
$N$ tests is represented as a Constraint Satisfaction Problem (CSP) instance,
which is in turn solved by a modern CSP solver. The results of using the
proposed approach reveal many $(\overline{1}, t)$-locating arrays that are
smallest known so far. In addition, some of these arrays are proved to be
minimum.
