Engeln, UlrikeDhungana, DeepakLambers, LeenBonorden, LeifHenning, Sören2024-02-142024-02-142024https://dl.gi.de/handle/20.500.12116/43510Code 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.encode smells detectionmachine learningmining sofware repositoriesText/Conference Paper10.18420/sw2024-ws_13