  Random testing (RT) is a black-box software testing technique that tests
programs by generating random test inputs. It is a widely used technique for
software quality assurance, but there has been much debate by practitioners
concerning its failure-detection effectiveness. RT is argued to be possibly
less effective by some researchers as it does not utilize any information about
the program under test. Efforts to mainly improve the failure-detection
capability of RT, have led to the proposition of Adaptive Random Testing (ART).
ART takes advantage of the location information of previous non-fault-detecting
test cases to enhance effectiveness as compared to RT. The approach has gained
popularity and has a large number of theoretical studies and methods that
employ different notions. In this review, our goal is to provide an overview of
existing ART studies. We classify all ART studies and assess existing ART
methods for numeric programs with a focus on their motivation, strategy, and
findings. The study also discusses several worthy avenues related to ART. The
review uses 109 ART papers in several journals, workshops, and conference
proceedings. The results of the review show that significant research efforts
have been made towards the field of ART, however further empirical studies are
still required to make the technique applicable in different test scenarios in
order to impact on the industry.
