KD-ART: Should we intensify or diversify tests to kill mutants?

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• 作者：Matthew Patrick ; ^a ; ^{mtp33@cam.ac.uk" class="auth_mail" title="E-mail the corresponding author} ; Yue Jia^b ; ^{yue.jia@ucl.ac.uk" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Mutation analysis ; Adaptive random testing ; Intensification and diversification
• 刊名：Information and Software Technology
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：81
• 期：Complete
• 页码：36-51
• 全文大小：1250 K

文摘

Context: Adaptive Random Testing (ART) spreads test cases evenly over the input domain. Yet once a fault is found, decisions must be made to diversify or intensify subsequent inputs. Diversification employs a wide range of tests to increase the chances of finding new faults. Intensification selects test inputs similar to those previously shown to be successful.

Objective: Explore the trade-off between diversification and intensification to kill mutants.

Method: We augment Adaptive Random Testing (ART) to estimate the Kernel Density (KD–ART) of input values found to kill mutants. KD–ART was first proposed at the 10th International Workshop on Mutation Analysis. We now extend this work to handle real world non numeric applications. Specifically we incorporate a technique to support programs with input parameters that have composite data types (such as arrays and structs).

Results: Intensification is the most effective strategy for the numerical programs (it achieves 8.5% higher mutation score than ART). By contrast, diversification seems more effective for programs with composite inputs. KD–ART kills mutants 15.4 times faster than ART.

Conclusion: Intensify tests for numerical types, but diversify them for composite types.