Detecting performance bad smells for Henshin model transformations

In model-driven software engineering, model transformations are used for the specification of model changes. Similar to programs also model transformations can exhibit bad smells which indicate possible weaknesses. In this paper, we address bad smells which can negatively affect the performance of the application of model transformations, particularly, model transformations defined in Henshin. Based on a description of the Henshin interpreter and its performance enhancing strategies, we describe a set of bad smells and corresponding detectors. We evaluate the detectors by applying them to the example rule set of Henshin.