Abstract
Software that is badly written and prone to design problems often smells. Code smells results in design anomalies that make software hard to understand and maintain. Several tools and techniques available in literature helps in detection of code smells. But the severity of the smells in the code is often not known immediately as it lacks visualization. In this paper, two method level code smells namely long method and feature envy are visualized using chernoff faces. Techniques proposed in literature either use knowledge driven approach or data driven approach for code smell detection. In the proposed approach a fusion of both knowledge and data driven approach is used to identify the most relevant features. These most relevant features are mapped to the 15 desired features of chernoff faces to visualize the behavior of the code. The result shows that almost 95% of the smells are visualized correctly. This helps in analyzing the programmer’s capability in maintaining quality of source code.
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Moiz, S.A., Chillarige, R.R. (2020). Method Level Code Smells: Chernoff Face Visualization. In: Satapathy, S.C., Raju, K.S., Shyamala, K., Krishna, D.R., Favorskaya, M.N. (eds) Advances in Decision Sciences, Image Processing, Security and Computer Vision. ICETE 2019. Learning and Analytics in Intelligent Systems, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-24322-7_63
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DOI: https://doi.org/10.1007/978-3-030-24322-7_63
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