For the testing of container classes and the algorithms or programs that operate on the data in a container, these data have the property of being homogeneous throughout the container. We have developed an approach for this situation called data coverage testing, where automated test generation can systematically generate increasing test data size. Given a program and a test model, it can be theoretically shown that there exists a sufficiently large test data set size N, such that testing with a data set size larger than N does not detect more faults. A number of experiments have been conducted using a set of C++ STL programs, comparing data coverage testing with two other testing strategies: statement coverage and random generation. These experiments validate the theoretical analysis for data coverage, confirming the predicted sufficiently large N for each program.
['Ponrudee Netisopakul', 'Lee J. White', 'John Morris', 'Daniel Hoffman']
Data coverage testing of programs for container classes