Selecting Software Test Data Using Data Flow Information
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering
Test data selection criteria for (software) integration testing
ISCI '90 Proceedings of the first international conference on systems integration on Systems integration '90
Toward quality data: an attribute-based approach
Decision Support Systems - Special issue on information technologies and systems
Communications of the ACM
A survey and analysis of Electronic Healthcare Record standards
ACM Computing Surveys (CSUR)
Data Quality: Concepts, Methodologies and Techniques (Data-Centric Systems and Applications)
Data Quality: Concepts, Methodologies and Techniques (Data-Centric Systems and Applications)
Beyond accuracy: what data quality means to data consumers
Journal of Management Information Systems
Methodologies for data quality assessment and improvement
ACM Computing Surveys (CSUR)
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In order to enable proper system and integration testing, it is often necessary to have huge test data inventories, reflecting the heterogeneous live system. Although the maintenance of large data stores can be guided by advice obtained from data quality evaluations, this technique can be only partly applied to test data inventories. Assessing test data quality is difficult, as the well-known data quality dimensions are not applicable in an easy fashion. For example, an otherwise good value of 100% for correctness would not allow to store erroneous test data items. The need for data quality dimensions dedicated to assessing test data quality can't be satisfied by well-known data quality dimensions. In this paper, we present our thesis approach to identify and validate new quality dimensions applicable for test data quality and develop quantification methods. We propose proximity to reality and degree of coverage as two new test data quality dimension and sketch quantification approach to measures, specifically suited for test data.