Detecting performance bad smells for henshin model transformations
Paper i proceeding, 2013
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.