Clustering classes in packages for program comprehension

• Main Authors: SUN, Xiaobing, LIU, Xiangyue, LI, Bin, LI, Bixin, LO, David, LIAO, Lingzhi
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Based clustering; Empirical studies; Latent dirichlet allocations; Latent Semantic Indexing; Probabilistic latent semantic analysis; Program comprehension; Software maintenance and evolution; Software project; Programming Languages and Compilers; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/3801; https://ink.library.smu.edu.sg/context/sis_research/article/4803/viewcontent/3787053.pdf
• Institution: Singapore Management University

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.