Description

ABSTRACT

The evolution of a software project is a rich data source for analyzing and improving the software development process. Recently, several research groups have tried to cluster source code artifacts based on information about how the code of a software system evolves. The results of these evolutionary approaches seem promising, but a direct comparison to traditional software clustering approaches based on structural code dependencies is still missing. To ll this gap, we conducted several clustering experiments with an established software clustering tool comparing and combining the evolutionary and the structural approach. These experiments show that the evolutionary approach could produce meaningful clustering results. While the traditional approach provides better results because of a more reliable data density of the structural data, the combination of both approaches is able to improve the overall clustering quality. A review of related studies shows that this approach of combining dependency information.