Data model extension impact analysis

Abstract

Relational database schemas are subject to change. For instance, columns of a table can be modified, deleted or extended. These changes have an impact on the source code that utilizes the corresponding table. They also have an impact on other database elements such as tables, views and stored procedures related to that table. Studies on change impact analysis so far mainly focus on the impact of changes on source code. There are a few tools that can analyze the impact of changes on database elements. However, these tools consider modifications and deletions, while ignoring extensions as a type of change. In this study, we propose an approach and a tool that can analyze the impact of data model extensions. Our approach involves the computation of a similarity score between each pair of tables in the database based on the unique column names defined in these tables. Tables that are similar to the extended tables beyond a threshold level are considered to be under impact. We perform an industrial case study to evaluate the accuracy of our approach and the effects of the threshold level on the accuracy of results.