Optimized Dependency Weights in Source Code Clustering

Abstract

Some methods use the dependencies between source code entities to perform clustering to, e.g., automatically map to an intended modular architecture or reconstruct the implemented software architecture. However, there are many different ways that source code entities can depend on each other in an object-oriented system, and it is not likely that all dependencies are equally useful. We investigate how well an optimized set of weights for 14 different types of dependencies perform when automatically mapping source code to modules using an established mapping technique. The optimized weights were found using genetic optimization. We compare the F1 score of precision and recall to uniform weights and weights computed by module relation ratio in eight open-source systems to evaluate performance. Our experiments show that optimized weights significantly outperform the others, especially in systems that seem not to have been designed using the low coupling, high cohesion principle. We also find that dependencies based on method calls are not useful for automatic mapping in any of the eight systems.