Code Smell Detection using Features from Version History

dc.contributor.author	Engeln, Ulrike
dc.contributor.editor	Herrmann, Andrea
dc.date.accessioned	2024-02-22T10:39:10Z
dc.date.available	2024-02-22T10:39:10Z
dc.date.issued	2023
dc.description.abstract	Code smells are indicators for bad quality of source code. A well suited approach for the development of a smell detector are machine learning techniques that learn based on features, i.e., measurable properties of the software under investigation, e.g., code metrics. One major objective of our machine learning approach is to decide how to express information from the version history by features. we introduce a method to draw historical features that improve smell detection.	en
dc.identifier.issn	0720-8928
dc.identifier.uri	https://dl.gi.de/handle/20.500.12116/43665
dc.language.iso	en
dc.pubPlace	Bonn
dc.publisher	Gesellschaft für Informatik e.V.
dc.relation.ispartof	Softwaretechnik-Trends Band 43, Heft 2
dc.relation.ispartofseries	Softwaretechnik-Trends
dc.subject	code smell
dc.subject	detection
dc.subject	version history
dc.subject	machine learning
dc.subject	code metrics
dc.title	Code Smell Detection using Features from Version History	en
dc.type	Text/Conference Paper
mci.conference.date	44993
mci.conference.location	Bad Honnef, Germany
mci.conference.sessiontitle	25. Workshop Software-Reengineering und -Evolution der GI-Fachgruppe Software Reengineering (SRE)
mci.reference.pages	38-39

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