Difference between revisions of "Clustering Source Code Elements by Semantic Similarity Using Wikipedia"
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| + | | title = Clustering Source Code Elements by Semantic Similarity Using Wikipedia | ||
| + | | date = 2015 | ||
| + | | authors = [[Mirco Schindler]]<br />[[Oliver Fox]]<br />[[Andreas Rausch]] | ||
| + | | doi = 10.1109/RAISE.2015.10 | ||
| + | | link = http://dl.acm.org/citation.cfm?id=2820672 | ||
| + | | plink = https://www.semanticscholar.org/paper/Clustering-Source-Code-Elements-by-Semantic-Using-Schindler-Fox/5a42064c49c927b1f9baed6e78b5bed4cf5e7707/figure/4 | ||
| + | }} | ||
'''Clustering Source Code Elements by Semantic Similarity Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Mirco Schindler]], [[Oliver Fox]] and [[Andreas Rausch]]. | '''Clustering Source Code Elements by Semantic Similarity Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[Mirco Schindler]], [[Oliver Fox]] and [[Andreas Rausch]]. | ||
== Overview == | == Overview == | ||
For humans it is no problem to determine if two words have a high or low [[semantic similarity]] in a given context. But is it possible to support a software developer or architect by using semantic data extracted from source code in the same way other relations like typical source code relations do? To answer this question authors developed an approach to compute the semantic similarity by using [[Wikipedia]] as a textual corpus. In a case study authors demonstrate this approach with a manageable software system. The results of using semantic similarities are compared with the outcome of using source code relations instead. | For humans it is no problem to determine if two words have a high or low [[semantic similarity]] in a given context. But is it possible to support a software developer or architect by using semantic data extracted from source code in the same way other relations like typical source code relations do? To answer this question authors developed an approach to compute the semantic similarity by using [[Wikipedia]] as a textual corpus. In a case study authors demonstrate this approach with a manageable software system. The results of using semantic similarities are compared with the outcome of using source code relations instead. | ||
Revision as of 22:59, 16 July 2019
Authors | Mirco Schindler Oliver Fox Andreas Rausch |
|---|---|
Publication date | 2015 |
DOI | 10.1109/RAISE.2015.10 |
Links | Original Preprint |
Clustering Source Code Elements by Semantic Similarity Using Wikipedia - scientific work related to Wikipedia quality published in 2015, written by Mirco Schindler, Oliver Fox and Andreas Rausch.
Overview
For humans it is no problem to determine if two words have a high or low semantic similarity in a given context. But is it possible to support a software developer or architect by using semantic data extracted from source code in the same way other relations like typical source code relations do? To answer this question authors developed an approach to compute the semantic similarity by using Wikipedia as a textual corpus. In a case study authors demonstrate this approach with a manageable software system. The results of using semantic similarities are compared with the outcome of using source code relations instead.
