Abstract
Database normalization is one of the main principles for designing relational databases, which is the most popular database model, with the objective of improving data and system qualities, such as performance. Refactoring the database for normalization can be costly, if the benefits of the exercise are not justified. Developers often ignore the normalization process due to the time and expertise it requires, introducing technical debt into the system. Technical debt is a metaphor that describes trade-offs between short-term goals and applying optimal design and development practices. We consider database normalization debts are likely to be incurred for tables below the fourth normal form. To manage the debt, we propose a multi-attribute analysis framework that makes a novel use of the Portfolio Theory and the TOPSIS method (Technique for Order of Preference by Similarity to Ideal Solution) to rank the candidate tables for normalization to the fourth normal form. The ranking is based on the tables estimated impact on data quality, performance, maintainability, and cost. The techniques are evaluated using an industrial case study of a database-backed web application for human resource management. The results show that the debt-aware approach can provide an informed justification for the inclusion of critical tables to be normalized, while reducing the effort and cost of normalization.