Clustering classes in packages for program comprehension

Clustering classes in packages for program comprehension

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 compr...

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Bibliographic Details
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
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Institution: Singapore Management University
Language: English
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Summary: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.

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