An empirical study on clone consistency prediction based on machine learning

Abstract

Code Clones have been accepted as a common phenomenon in software, thanks to the increasing demand for rapid production of software. The existence of code clones is recognized by developers in the form of clone group, which includes several pieces of clone fragments that are similar to one another. A change in one of these clone fragments may indicate necessary “consistent changes” are required for the rest of the clones within the same group, which can increase extra maintenance costs. A failure in making such consistent change when it is necessary is commonly known as a “clone consistency-defect”, which can adversely impact software maintainability.