In this paper, we introduce an enhanced form of random testing called Adaptive Random Testing. Adaptive random testing seeks to distribute test cases more evenly within the input space. It is based on the intuition that for non-point types of failure patterns, an even spread of test cases is more likely to detect failures using fewer test cases than ordinary random testing. Experiments are performed using published programs. Results show that adaptive random testing does outperform ordinary random testing significantly (by up to as much as 50%) for the set of programs under study. These results are very encouraging, providing evidences that our intuition is likely to be useful in improving the effectiveness of random testing.
Lecture Notes in Computer Science: Advances in Computer Science: proceedings of the 9th Asian Computing Science Conference: Higher-Level Decision Making (ASIAN 2004), Chiang Mai, Thailand, 08-10 December 2004 / M. J. Maher (ed.),
Vol. 3321, pp. 320-329