Adaptive random testing based on flexible partitioning

Abstract

Adaptive random testing (ART) achieves better failure‐detection effectiveness than random testing due to its even spreading of test cases. ART by random partitioning (RP‐ART) is a lightweight method, but its advantage over random testing is relatively low. Although iterative partition testing (IPT) method has good performance for detecting failures in a block pattern, it loses randomness during the test case generation. To overcome the shortcomings of the above two algorithms, a new algorithm named ART by flexible partitioning (FP‐ART) is proposed. In the FP‐ART, a set of random candidates is used to select an appropriate test case by considering their boundary distance. Accordingly, the corresponding sub‐domain is also partitioned by the new test case. Based on this kind of flexible partitioning, the randomness of test case selection can be guaranteed and the spatial distribution of test cases is even more diverse. According to the results in simulation and empirical experiments, FP‐ART demonstrates better failure‐detection effectiveness than RP‐ART and is more suitable to detect the failures in strip patterns than the IPT method. Meanwhile, its failure‐detection ability is much stronger than that of fixed‐size‐candidate‐set ART in the cases of a relatively high failure rate.

A Survey on Adaptive Random Testing

“…Similarly, Mayer [96], [97], for PBS with random and bisection partitioning, used exclusion regions in a possible subdomain to generate a new test case. Mao [98], to overcome the drawbacks of random partitioning, proposed a new partitioning schema, twopoint partitioning, based on the STFCS test-case-identification component: When generating a new test case tc, it randomly chooses two candidates from the subdomain that needs to be partitioned, and then uses the midpoint of tc and the farthest candidate as the break point to partition the subdomain.…”

Enhanced Performance of Adaptive Random Partitioning Testing by Unifying the ARPT-1 and ARPT-2 Strategies

“…A new Adaptive Random Testing based on Two-Point Partitioning (ART-TPP) algorithm [5] was proposed to improve the software testing process. It mainly concerned with the partitioning-based adaptive random testing.…”

One-Domain-One-Input: Adaptive Random Testing by Orthogonal Recursive Bisection With Restriction

“…The element from the candidate set that is furthest from the executed tests is then chosen as the next test input. The underlying distance strategy of FSCS-ART has been used in several ART implementations [27], [28], [29].…”

“…Another partition-based ART approach is two-point partitioning ART (ART-TPP) [28], which augments the ART by balancing approach [29] by applying a different test case selection criterion and using the midpoint of the test inputs to further partition the domain. ART-TPP selects the largest partition and, if there is no executed test there already, it randomly generates a test input.…”