Auflistung nach Autor:in "Engeln, Ulrike"
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- KonferenzbeitragCode Smell Detection using Features from Version History(Softwaretechnik-Trends Band 43, Heft 2, 2023) Engeln, UlrikeCode 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.
- KonferenzbeitragCode Smell Detection using Features from Version History(SE 2024 - Companion, 2024) Engeln, UlrikeCode 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.