Generating and evaluating choices for fixing inconsistencies in UML design models

Our objective is to provide automated support for assisting designers in fixing inconsistencies in UML models. We have previously developed techniques for efficiently detecting inconsistencies in such models and identifying where changes need to occur in order to fix problems detected by these means. This paper extends previous work by describing a technique for automatically generating a set of concrete changes for fixing inconsistencies and providing information about the impact of each change on all consistency rules. The approach is integrated with the design tool IBM Rational RoseTM. We demonstrate the computational scalability and usability of the approach through the empirical evaluation of 39 UML models of sizes up to 120,000 elements