Measuring software design complexity
Journal of Systems and Software
Systems design in a database environment
Systems design in a database environment
DoD legacy systems: reverse engineering data requirements
Communications of the ACM
An approach for reverse engineering of relational databases
Communications of the ACM
Anchoring data quality dimensions in ontological foundations
Communications of the ACM
Communications of the ACM
A product perspective on total data quality management
Communications of the ACM
Data quality and systems theory
Communications of the ACM
Assessing data quality in accounting information systems
Communications of the ACM
Data Quality for the Information Age
Data Quality for the Information Age
Metrics and Models in Software Quality Engineering
Metrics and Models in Software Quality Engineering
Observed idiosyncracies of relational database designs
WCRE '95 Proceedings of the Second Working Conference on Reverse Engineering
Strategies for Data Reengineering
WCRE '02 Proceedings of the Ninth Working Conference on Reverse Engineering (WCRE'02)
Reverse Engineering of Test Cases for Selective Regression Testing
CSMR '04 Proceedings of the Eighth Euromicro Working Conference on Software Maintenance and Reengineering (CSMR'04)
From DQ to EQ: understanding data quality in the context of e-business systems
Communications of the ACM - The digital society
Principles of Program Design
An introduction to database systems (The Systems programming series)
An introduction to database systems (The Systems programming series)
Testing a Datawarehouse - An Industrial Challenge
TAIC-PART '06 Proceedings of the Testing: Academic & Industrial Conference on Practice And Research Techniques
The Theory and Practice of Foundation Testing
IEEE Software
A model of large program development
IBM Systems Journal
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Abstract: This industrial report stems from practical experience in assessing the quality of customer databases. The process it describes unites three automated audits, - an audit of the database schema, an audit of the database structure and an audit of the database content. The audit of the database schema checks for design smells and rule violations. The audit of the database structure measures the size, complexity and quality of the database model. The audit of the database content processes the data itself to uncover invalid data values, missing records and redundant records. The purpose of these audits is to assess the quality of the database and to determine whether a data reengineering or data clean-up project is required.