Defining and Validating Metrics for Assessing the Maintainability of Entity-Relationship Diagrams

Database and data model evolution is a significant problem in the highly dynamic business environment that we experience these days. To support the rapidly changing data requirements of agile companies, conceptual data models, which constitute the foundation of database design, should be sufficiently flexible to be able to incorporate changes easily and smoothly. In order to understand what factors drive the maintainability of conceptual data models and to improve conceptual modelling processes, we need to be able to assess conceptual data model properties and qualities in an objective and cost-efficient manner. The scarcity of early available and thoroughly validated maintainability measurement instruments motivated us to define a set of metrics for Entity-Relationship (ER) diagrams, which are a relevant graphical formalism of the conceptual data modelling method. In this paper we show that these objectives and easily calculated metrics, measuring internal properties of ER diagrams related to their structural complexity, can be used as indirect measures (hereafter called indicators) of the maintainability of the diagrams. These metrics may replace more expensive, subjective, and hence potentially unreliable maintainability measurement instruments that are based on expert judgement. Moreover, these metrics are an alternative to direct measurements that can only be obtained during the actual process of data model maintenance. Another result is that the validation of the metrics as early maintainability indicators opens up the way for an in-depth study of structural complexity as a major determinant of conceptual data model maintainability. Apart from the definition of a metrics suite, a contribution of this study is the methodological approach that was followed to theoretically validate the proposed metrics as structural complexity measures and to empirically validate them as maintainability indicators. This approach is based both on Measurement Theory and on an experimental research methodology, stemming mainly from current research in the field of empirical software engineering. In the paper we specifically emphasize the need to conduct a family of related experiments, improving and confirming each other, to produce relevant, empirically supported knowledge on the validity and usefulness of metrics.