Data Fusion for Software Remodularization

Abstract

Software refactoring aims at optimizing software modularization by improving internal software structure without altering its external behavior. There exists various approaches for suggesting refactoring opportunities, based on different sources of information, e.g., structural, semantic, and historical. In this paper, we propose a data fusion model to combine different sources of information in order to identify refactoring opportunities and we instantiate it to support Move Class refactoring. We report the results of our validation conducted on four software systems and we show that our proposal improves the modularization quality by 29% and that our tool is able to provide meaningful recommendations for move class refactoring. Specifically, more than 70% of the recommendations were considered meaningful from the developers' point of view.