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We showed that training impacts god class detection.
Specifically, it focuses on factors affecting god class detection, one of the most known code smells.
FinG is proposed to explore a set of aspects related to god class detection.
All analysis procedure we adopted indicates that software size impacts agreement on god class detection.
Lanza and Marinescu (2005) proposed a heuristic for god class detection.
The experimenter, who was one of the oracle researchers, defined the god class detection strategy.
We found a design comprehension tool support does not improve agreement on god class detection.
One can argues that this might to affect the god class detection.
Specifically, we addressed cause-effect relation between five factors and the agreement on god class detection.
This reinforces our idea that god class detection is strongly affected by personal conceptualizations.
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What's more, this research deduces the relation between cost-sensitive parameter and in-class detection rate, and designs LCSDM to obtain balanced detection rate.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com