Several code smells detection tools have been developed providing different results, because smells can be subjectively interpreted and hence detected in different ways. Usually the detection techniques are based on the computation of different kinds of metrics, and other aspects related to the domain of the system under analysis, its size and other design features are not taken into account. In this paper we propose an approach we are studying based on machine learning techniques. We outline some common problems faced for smells detection and we describe the different steps of our approach and the algorithms we use for the classification.
ARCELLI FONTANA, F., Zanoni, M., Marino, A., Mäntylä, M. (2013). Code Smell Detection: towards a Machine Learning-based Approach. In Proceedings of the 29th IEEE International Conference on Software Maintenance (ICSM 2013), ERA Track (pp.396-399). IEEE Computer Society [10.1109/ICSM.2013.56].
Code Smell Detection: towards a Machine Learning-based Approach
ARCELLI FONTANA, FRANCESCA;ZANONI, MARCO;
2013
Abstract
Several code smells detection tools have been developed providing different results, because smells can be subjectively interpreted and hence detected in different ways. Usually the detection techniques are based on the computation of different kinds of metrics, and other aspects related to the domain of the system under analysis, its size and other design features are not taken into account. In this paper we propose an approach we are studying based on machine learning techniques. We outline some common problems faced for smells detection and we describe the different steps of our approach and the algorithms we use for the classification.
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