Semantic clustering: Identifying topics in source code

By Adrian Kuhn, Stéphane Ducasse, Tudor Gîrba. Information and Software Technology, 2007

545 citations recorded by the source corpus.

Subfields: Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ba2cfa6c1bb1beffef09eea59adfea7e280a5003

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Citations in the corpus

On the localness of software (2014, background): “Identifiers take up the majority of source code tokens [9], hence play an important role in software engineering in both code cognition [31] and understanding [28].”
Software Architecture Reconstruction: A Process-Oriented Taxonomy (2009, methodology): “Some approaches use the symbolic information available in the comments [127], [128] or in the method names [89], [102].”
Automatic generation of natural language summaries for Java classes (2013, methodology): “, reverse engineering), Kuhn [22] used TR techniques to extracts words to label source code clusters.”
Retrieval from software libraries for bug localization: a comparative study of generic and composite text models (2011, methodology): “It is therefore not surprising that IR based methods are being applied in other areas of software maintenance and program comprehension [19, 6, 21, 9, 13, 18].”
On the Use of Automated Text Summarization Techniques for Summarizing Source Code (2010, methodology): “Other related work includes [26], where TR techniques are used to cluster source code and relevant terms from each cluster are extracted to form labels.”
Mining source code repositories at massive scale using language modeling (2013, background): “Finally, the role of identiﬁers has been studied by Liblit et al [13] from a cognitive perspective, while their importance in code understanding has been identiﬁed by Kuhn et al. [14].”
Supporting program comprehension with source code summarization (2010, background): “The closest is the work in [4], where LSI is used to cluster source code and then relevant terms from each cluster are extracted to form labels.”