Conclusion
Although there are various studies related to selecting test cases, few are available for both path coverage and coverage balance. Our method is to select test cases that both traverse target paths and achieve coverage balance to improve the fault detection rate. We formulate the problem as an evolution selection by applying GA. Experimental results show that our method can effectively improve the fault detection rate of the selected test cases while ensuring the reduction rate. It can select a subset of test cases that meet testing requirements with high efficiency.
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Acknowledgements
This work was jointly funded by the Research Projects of Basic Scientific Research Business Expenses in Institutions of Higher Learning of Heilongjiang Province (1353ZD003 and 2018-KYYWFMY-0104); Science and Technology Research Project of Mudanjiang Normal University (YB2019003); the Scientific and Technological Plan Project of Mudanjiang City (Z2018g023); and the Innovation Foundation of Science and Technology of Dalian (2018J12GX045).
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Ma, B., Wan, L., Yao, N. et al. Evolutionary selection for regression test cases based on diversity. Front. Comput. Sci. 15, 152205 (2021). https://doi.org/10.1007/s11704-020-9229-3
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DOI: https://doi.org/10.1007/s11704-020-9229-3