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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2017-01-01
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| Series: | Scientific Programming |
| Online Access: | http://dx.doi.org/10.1155/2017/3787053 |
| 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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| ISSN: | 1058-9244 1875-919X |