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Code smells indicate poor implementation choices that may hinder the system maintenance. Their detection is important for the software quality improvement, but studies suggest that it should be tailored to the perception of each developer. Therefore, detection techniques must adapt their strategies to the developer’s perception. Machine Learning (ML) algorithms is a promising way to customize the smell detection, but there is a lack of studies on their accuracy in detecting smells for different developers. This paper evaluates the use of MLalgorithms on detecting code smells for different developers, considering their individual perception about code smells. We experimentally compared the accuracy of 6 algorithms in detecting 4 code smell types for 40 different developers. For this, we used a detailed dataset containing instances of 4 code smell types manually validated by 40 developers. The results show that ML-algorithms achieved low accuracies for the developers that participated of our study, showing that are very sensitive to the smell type and the developer. These algorithms are not able to learn with limited training set, an important limitation when dealing with diverse perceptions about code smells.

Evaluating the Accuracy of Machine Learning Algorithms on Detecting Code Smells for Different Developers