Metric space-based test-base adequacy criteria
The Computer Journal
The evaluation of program-based software test data adequacy criteria
Communications of the ACM
Comparing test data adequacy criteria
ACM SIGSOFT Software Engineering Notes
Systems testing and statistical test data coverage
COMPSAC '97 Proceedings of the 21st International Computer Software and Applications Conference
Reinforced Condition/Decision Coverage (RC/DC): A New Criterion for Software Testing
ZB '02 Proceedings of the 2nd International Conference of B and Z Users on Formal Specification and Development in Z and B
Object distance and its application to adaptive random testing of object-oriented programs
Proceedings of the 1st international workshop on Random testing
FATES'04 Proceedings of the 4th international conference on Formal Approaches to Software Testing
Refactoring and metrics for TTCN-3 test suites
SAM'06 Proceedings of the 5th international conference on System Analysis and Modeling: language Profiles
Analyzing Response Inconsistencies in Test Suites
TESTCOM '09/FATES '09 Proceedings of the 21st IFIP WG 6.1 International Conference on Testing of Software and Communication Systems and 9th International FATES Workshop
Achieving scalable model-based testing through test case diversity
ACM Transactions on Software Engineering and Methodology (TOSEM)
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Test effectiveness is a central quality aspect of a test specification which reflects its ability to demonstrate system quality levels and to discover system faults. A well-known approach for its estimatation is to determine coverage metrics for the system code or system model. However, often these are not available as such but the system interface only, which basically define structural aspects of the stimuli and responses to the system. Therefore, this paper focuses on the idea of using test data variance analysis as another analytical approach to determine test quality. It presents a method for the quantitative evaluation of structural and semantical variance of test data. Test variance is defined as the test data distribution over the system interface data domain. It is expected that the more the test data varies, the better the system is tested by a given test suite. The paper instantiates this method for black-box test specifications written in TTCN-3 and the structural analysis of send templates. Distance metrics and similarity relations are used to determine the data variance.