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Code smell detection using features from version history
Publikationstyp
Conference Paper
Date Issued
2023
Sprache
English
Author(s)
Engeln, Ulrike
Journal
Volume
43
Citation
25. Workshop Software-Reengineering und -Evolution (WSRE 2023)
Contribution to Conference
Publisher
GI
Code smells are indicators of bad quality in software. There exist several detection techniques for smells, which mainly base on static properties of the source code. Those detectors usually show weak performance in detection of context-sensitive smells since static properties hardly
capture information about relations in the code. To address this information gap, we propose a strategy to extract information about interdependencies from version history. We use static and the new historical features to identify code smells by a random forest. Experiments show that the introduced historical features improve detection of code smells that focus on interdependencies.
capture information about relations in the code. To address this information gap, we propose a strategy to extract information about interdependencies from version history. We use static and the new historical features to identify code smells by a random forest. Experiments show that the introduced historical features improve detection of code smells that focus on interdependencies.
DDC Class
004: Computer Sciences