Publication Type

Journal Article

Publication Date

4-2017

Abstract

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.

Keywords

Based clustering, Empirical studies, Latent dirichlet allocations, Latent Semantic IndexingProbabilistic latent semantic analysisProgram comprehensionSoftware maintenance and evolutionSoftware project

Discipline

Programming Languages and Compilers | Software Engineering

Research Areas

Cybersecurity

Publication

Scientific Programming

ISSN

1058-9244

Identifier

10.1155/2017/3787053

Publisher

IOS Press / Hindawi Publishing Corporation

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org./10.1155/2017/3787053

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