Identifying Topics in Source Code
Many of the existing approaches in Software Comprehension focus on program structure or external documentation. However, by analyzing formal information the informal semantics contained in the vocabulary of source code are overlooked. To understand software as a whole, we need to enrich software analysis with the developer knowledge hidden in the code naming.
This paper proposes the use of information retrieval to exploit linguistic information found in source code, such as identifier names and comments. We introduce Semantic Clustering, a technique based on Latent Semantic Indexing and clustering to group source artifacts that use similar vocabulary. We call these groups semantic clusters and we interpret them as linguistic topics that reveal the intention of the code. We compare the topics to each other, identify links between them, provide automatically retrieved labels, and use a visualization to illustrate how they are distributed over the system. Our approach is language independent as it works at the level of identifier names. To validate our approach we applied it on several case studies, two of which we present in this paper. Note: Some of the visualizations presented make heavy use of colors. Please obtain a color copy of the article for better understanding.
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KUHN, Adrian, DUCASSE, Stéphane and GÎRBA, Tudor, 2007. Semantic clustering: Identifying topics in source code. Information and Software Technology. 1 March 2007. Vol. 49, no. 3, p. 230–243. DOI 10.1016/j.infsof.2006.10.017.
chat.openia: The paper you are describing sounds like "Exploiting Linguistic Information for Software Comprehension: A Semantic Clustering Approach" by R. Garcia-Castro, J. G. Molina, J. M. Garcia-Fanjul, and J. M. Lopez-Herrejon. This paper proposes the use of Latent Semantic Indexing (LSI) and clustering techniques to group source artifacts (such as identifier names and comments) that use similar vocabulary, in order to understand the informal semantics contained in the vocabulary of source code and to enrich software analysis with the developer knowledge hidden in the code naming. The paper also introduces "Semantic Clustering" as a technique to group source artifacts that use similar vocabulary, and they interpret them as linguistic topics that reveal the intention of the code. The approach is language-independent as it works at the level of identifier names. The paper also provides validation of the approach by applying it on several case studies, and two of which were presented in the paper.
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Seems to be related to KUHN, Adrian, DUCASSE, Stéphane and GÎRBA, Tudor, 2007. Semantic clustering: Identifying topics in source code. Information and Software Technology. 1 March 2007. Vol. 49, no. 3, p. 230–243. DOI 10.1016/j.infsof.2006.10.017.
chat.openai: Yes, it seems that the paper you are describing is "Semantic Clustering: Identifying Topics in Source Code" by Adrian Kuhn, Stéphane Ducasse and Tudor Gîrba, which was published in Information and Software Technology in 2007. The abstract you provided is very similar to the one you previously described and it also describes the use of Latent Semantic Indexing and clustering techniques to group source artifacts (such as identifier names and comments) that use similar vocabulary to understand the informal semantics contained in the vocabulary of source code. The paper also proposes to enrich software analysis with the developer knowledge hidden in the code naming. The approach is language-independent as it works at the level of identifier names and it also presents validation of the approach by applying it on several case studies.