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Code smells are seen as major source of technical debt and, as such, shouldbe detected and removed. However, researchers argue that the subjectiveness ofthe code smells detection process is a major hindrance to mitigate the problemof smells-infected code. We proposed the crowdsmelling approach based onsupervised machine learning techniques, where the wisdom of the crowd (ofsoftware developers) is used to collectively calibrate code smells detectionalgorithms, thereby lessening the subjectivity issue. This paper presents theresults of a validation experiment for the crowdsmelling approach. In thecontext of three consecutive years of a Software Engineering course, a total"crowd" of around a hundred teams, with an average of three members each,classified the presence of 3 code smells (Long Method, God Class, and FeatureEnvy) in Java source code. These classifications were the basis of the oraclesused for training six machine learning algorithms. Over one hundred models weregenerated and evaluated to determine which machine learning algorithms had thebest performance in detecting each of the aforementioned code smells. Goodperformances were obtained for God Class detection (ROC=0.896 for Naive Bayes)and Long Method detection (ROC=0.870 for AdaBoostM1), but much lower forFeature Envy (ROC=0.570 for Random Forrest). Obtained results suggest thatcrowdsmelling is a feasible approach for the detection of code smells, butfurther validation experiments are required to cover more code smells and toincrease external validity.

Crowdsmelling: The use of collective knowledge in code smells detection