Kernel Density Adaptive Random Testing

Mutation analysis is used to assess the effectiveness of a test data generation technique at finding faults. Once a mutant is killed, decisions must be made whether to diversify or intensify the subsequent test inputs. Diversification employs a wide range of test inputs with the aim of increasing the chances of killing new mutants. By contrast, intensification selects test inputs which are similar to those previously shown to be successful, taking advantage of overlaps in the conditions under which mutants can be killed. This paper explores the trade-off between diversification and intensification by augmenting Adaptive Random Testing (ART) to estimate the Kernel Density (KD-ART) of input values which are found to kill mutants. The results suggest that intensification is typically more effective at finding faults than diversification. KD-ART (intensify) achieves 7.24% higher mutation score on average than KD-ART (diversify). Moreover, KD-ART is computationally less expensive than ART. The new technique requires an average 5.98% of the time taken before.