TDQMed: Managing Collections of Complex Test Data
Medical devices like Medical Linear Accelerators (LINAC) are extensively tested before they are used in routine practice. Such systems typically interact with multiple other systems that produce complex input data, like medical images annotated with extensive metadata. Before such a system is actually used in a hospital with real patients it has to be tested with test data as realistic as possible. Suitable test data, however, cannot be easily generated. For this reason vendors typically accumulate large collections of patient files over the years to have them available for various test scenarios. In the TDQMed project we have developed methods and tools that enable a tester to estimate both the quality of a test data collection and its applicability for a particular test goal. A prototype system has been implemented to demonstrate the feasibility of measuring specific test data related quality criteria like coverage of test space and closeness to reality. An evaluation with professional testers indicates that the overall approach is promising.
KeywordsTest-data quality Knowledge discovery Information extraction and integration
This project is supported by the German Federal Ministry of Education and Research (BMBF), project grant No. 01EX1013G.
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