Abstract
Test data generation using traditional software testing methods generally requires considerable manual effort and generates only a limited number of test cases before the amount of time expanded becomes unacceptably large. A rule-based framework that will automatically generate test data to achieve maximal branch coverage is presented. The design and discovery of rules used to generate meaningful test cases are also described. The rule-based approach allows this framework to be extended to include additional testing requirements and test case generation knowledge.
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This work was supported in part by George C. Marshall Space Flight Center, NASA/MSFC, AL 35812 (NASA-NCC8-14).
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Chang, KH., Cross, J.H., Carlisle, W.H. et al. A framework for intelligent test data generation. J Intell Robot Syst 5, 147–165 (1992). https://doi.org/10.1007/BF00444293
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DOI: https://doi.org/10.1007/BF00444293