The problem of conceptualization in god class detection: agreement, strategies and decision drivers

The concept of code smells is widespread in Software Engineering. Despite the empirical studies addressing the topic, the set of context-dependent issues that impacts the human perception of what is a code smell has not been studied in depth. We call this the code smell conceptualization problem. To discuss the problem, empirical studies are necessary. In this work, we focused on conceptualization of god class. God class is a code smell characterized by classes that tend to centralize the intelligence of the system. It is one of the most studied smells in software engineering literature. A controlled experiment that extends and builds upon a previous empirical study about how humans detect god classes, their decision drivers, and agreement rate. Our study delves into research questions of the previous study, adding visualization to the smell detection process, and analyzing strategies of detection. Our findings show that agreement among participants is low, which corroborates previous studies. We show that this is mainly related to agreeing on what a god class is and which thresholds should be adopted, and not related to comprehension of the programs. The use of visualization did not improve the agreement among the participants. However, it did affect the choice of detection drivers. This study contributes to expand empirical evidences on the impact of human perception on detecting code smells. It shows that studies about the human role in smell detection are relevant and they should consider the conceptualization problem of code smells.