Abstract-Random testing (RT) has been widely used in the testing of various software and hardware systems. Adaptive random testing (ART) is a family of random testing techniques that aim to enhance the failure-detection effectiveness of RT by spreading random test cases evenly throughout the input domain. ART has been empirically shown to be effective on software with numeric inputs. However, there are two aspects of ART that need to be addressed to render its adoption more widespread-applicability to programs with nonnumeric inputs, and the high computation overhead of many ART algorithms. We present a linear-order ART algorithm for software with non-numeric inputs. The key requirement for using ART with non-numeric inputs is an appropriate "distance" measure. We use the concepts of categories and choices from category-partition testing to formulate such a measure. We investigate the failure-detection effectiveness of our technique by performing an empirical study on 14 object programs, using two standard metrics-F-measure and P-measure. Our ART algorithm statistically significantly outperforms RTon 10 of the 14 programs studied, and exhibits performance similar to RTon three of the four remaining programs. The selection overhead of our ART algorithm is close to that of RT.
Furthermore, a linear-order ART algorithm was proposed REF , which takes advantage of the properties of the distance measure that is based on the concepts of categories and choices to achieve linear-time test case selection overhead.
A Cost-Effective Random Testing Method for Programs with Non-Numeric Inputs
IEEE Transactions on Computers
IEEE Transactions on Computers
Computer Science