PPIG 2011 - 23rd Annual Workshop
An Investigation into Qualitative Human Oracle Costs
Sheeva Afshan, Phil McMinn
Abstract: The test data produced by automatic test data generators are often `unnatural' particularly for the programs that make use of human-recognisable variables such as 'country', 'name', 'date', 'time', 'age' and so on. The test data generated for these variables are usually arbitrary-looking values that are complex for human testers to comprehend and evaluate. This is due to the fact that automatic test data generators have no domain knowledge about the program under test and thus the test data they produce are hardly recognised by human testers. As a result, the tester is likely to spend additional time in order to understand such data. This paper demonstrates how the incorporation of some domain knowledge into an automatic test data generator can significantly improve the quality of the generated test data. Empirical studies are proposed to investigate how this incorporation of knowledge can reduce the overall testing costs.