Clustering Classes in Packages for Program Comprehension

Abstract EN

During software maintenance and evolution, one of the important tasks faced by developers is to understand a system quickly and accurately.

With the increasing size and complexity of an evolving system, program comprehension becomes an increasingly difficult activity.

Given a target system for comprehension, developers may first focus on the package comprehension.

The packages in the system are of different sizes.

For small-sized packages in the system, developers can easily comprehend them.

However, for large-sized packages, they are difficult to understand.

In this article, we focus on understanding these large-sized packages and propose a novel program comprehension approach for large-sized packages, which utilizes the Latent Dirichlet Allocation (LDA) model to cluster large-sized packages.

Thus, these large-sized packages are separated as small-sized clusters, which are easier for developers to comprehend.

Empirical studies on four real-world software projects demonstrate the effectiveness of our approach.

The results show that the effectiveness of our approach is better than Latent Semantic Indexing- (LSI-) and Probabilistic Latent Semantic Analysis- (PLSA-) based clustering approaches.

In addition, we find that the topic that labels each cluster is useful for program comprehension.