Automated Refactoring of OCL Constraints with Search

Object Constraint Language (OCL) constraints are typically used to provide precise semantics to models developed with the Unified Modeling Language (UML). When OCL constraints evolve regularly, it is essential that they are easy to understand and maintain. For instance, in cancer registries, to ensure the quality of cancer data, more than one thousand medical rules are defined and evolve regularly. Such rules can be specified with OCL. It is, therefore, important to ensure the understandability and maintainability of medical rules specified with OCL. To tackle such a challenge, we propose an automated search-based OCL constraint refactoring approach (SBORA) by defining and applying four semantics-preserving refactoring operators (i.e., Context Change, Swap, Split and Merge) and three OCL quality metrics (Complexity, Coupling, and Cohesion) to measure the understandability and maintainability of OCL constraints. We evaluate SBORA along with six commonly used multi-objective search algorithms (e.g., Indicator-Based Evolutionary Algorithm (IBEA)) by employing four case studies from different domains: healthcare (i.e., cancer registry system from Cancer Registry of Norway (CRN)), Oil&Gas (i.e., subsea production systems), warehouse (i.e., handling systems), and an open source case study named SEPA. Results show: 1) IBEA achieves the best performance among all the search algorithms and 2) the refactoring approach along with IBEA can manage to reduce on average 29.25 percent Complexity and 39 percent Coupling and improve 47.75 percent Cohesion, as compared to the original OCL constraint set from CRN. To further test the performance of SBORA, we also applied it to refactor an OCL constraint set specified on the UML 2.3 metamodel and we obtained positive results. Furthermore, we conducted a controlled experiment with 96 subjects and results show that the understandability and maintainability of the original constraint set can be improved significantly from the perspectives of the 96 participants of the controlled experiment.