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Entropy 2018, 20(5), 372; https://doi.org/10.3390/e20050372

Software Code Smell Prediction Model Using Shannon, Rényi and Tsallis Entropies

1
Department of Computer Science and Engineering, Amity School of Engineering and Technology, New Delhi 110061, India
2
University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, New Delhi 110078, India
3
Department of Mathematics, Amity School of Engineering and Technology, New Delhi 110061, India
4
Center of Information and Communication Technology/Engineering (ICT/ICE) Research, New Building of Covenant University Center for Research Innovation and Development (CUCRID), Covenant University, Ota 112231, Nigeria
5
Department of Computer Engineering, Atilim University, Incek 06836, Turkey
6
Department of Software Engineering, Kaunas University of Technology, Kaunas 44249, Lithuania
*
Author to whom correspondence should be addressed.
Received: 27 January 2018 / Revised: 22 April 2018 / Accepted: 1 May 2018 / Published: 17 May 2018
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Abstract

The current era demands high quality software in a limited time period to achieve new goals and heights. To meet user requirements, the source codes undergo frequent modifications which can generate the bad smells in software that deteriorate the quality and reliability of software. Source code of the open source software is easily accessible by any developer, thus frequently modifiable. In this paper, we have proposed a mathematical model to predict the bad smells using the concept of entropy as defined by the Information Theory. Open-source software Apache Abdera is taken into consideration for calculating the bad smells. Bad smells are collected using a detection tool from sub components of the Apache Abdera project, and different measures of entropy (Shannon, Rényi and Tsallis entropy). By applying non-linear regression techniques, the bad smells that can arise in the future versions of software are predicted based on the observed bad smells and entropy measures. The proposed model has been validated using goodness of fit parameters (prediction error, bias, variation, and Root Mean Squared Prediction Error (RMSPE)). The values of model performance statistics ( R 2 , adjusted R 2 , Mean Square Error (MSE) and standard error) also justify the proposed model. We have compared the results of the prediction model with the observed results on real data. The results of the model might be helpful for software development industries and future researchers. View Full-Text
Keywords: software design defects; software quality; code smell; entropy; statistical model; regression software design defects; software quality; code smell; entropy; statistical model; regression
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Gupta, A.; Suri, B.; Kumar, V.; Misra, S.; Blažauskas, T.; Damaševičius, R. Software Code Smell Prediction Model Using Shannon, Rényi and Tsallis Entropies. Entropy 2018, 20, 372.

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