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

• Published in: Entropy (Basel, Switzerland) Vol. 20; no. 5; p. 372
• Main Authors: Gupta, Aakanshi, Suri, Bharti, Kumar, Vijay, Misra, Sanjay, Blažauskas, Tomas, Damaševičius, Robertas
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 17.05.2018; MDPI
• Subjects: code smell; Entropy; Entropy (Information theory); Goodness of fit; Information theory; Mathematical models; Open source software; Prediction models; regression; software design defects; Software quality; Software reliability; Source code; Standard error; statistical model; User requirements
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e20050372

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.