Python code smells detection using conventional machine learning models. | D2R Server publishing the DBLP Bibliography Database, hosted at L3S Research Center

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dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Hamoud_Aljamaan>
dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Rana_Sandouka>
foaf:homepage	<http://dx.doi.org/doi.org%2F10.7717%2Fpeerj-cs.1370>
foaf:homepage	<https://doi.org/10.7717/peerj-cs.1370>
dc:identifier	DBLP journals/peerj-cs/SandoukaA23 (xsd:string)
dc:identifier	DOI doi.org%2F10.7717%2Fpeerj-cs.1370 (xsd:string)
dcterms:issued	2023 (xsd:gYear)
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rdfs:label	Python code smells detection using conventional machine learning models. (xsd:string)
foaf:maker	<https://dblp.l3s.de/d2r/resource/authors/Hamoud_Aljamaan>
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swrc:pages	e1370 (xsd:string)
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rdfs:seeAlso	<http://dblp.uni-trier.de/db/journals/peerj-cs/peerj-cs9.html#SandoukaA23>
rdfs:seeAlso	<https://doi.org/10.7717/peerj-cs.1370>
dc:title	Python code smells detection using conventional machine learning models. (xsd:string)
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rdf:type	swrc:Article
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swrc:volume	9 (xsd:string)