Software Code Smell Prediction Model Using Shannon, Rényi and Tsallis Entropies
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 t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-05-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/20/5/372 |
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| author | Aakanshi Gupta Bharti Suri Vijay Kumar Sanjay Misra Tomas Blažauskas Robertas Damaševičius |
| author_facet | Aakanshi Gupta Bharti Suri Vijay Kumar Sanjay Misra Tomas Blažauskas Robertas Damaševičius |
| author_sort | Aakanshi Gupta |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T13:46:03Z |
| format | Article |
| id | doaj.art-3821a6cda5314a44aa1300eb496ad53f |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T13:46:03Z |
| publishDate | 2018-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-3821a6cda5314a44aa1300eb496ad53f2022-12-22T04:21:05ZengMDPI AGEntropy1099-43002018-05-0120537210.3390/e20050372e20050372Software Code Smell Prediction Model Using Shannon, Rényi and Tsallis EntropiesAakanshi Gupta0Bharti Suri1Vijay Kumar2Sanjay Misra3Tomas Blažauskas4Robertas Damaševičius5Department of Computer Science and Engineering, Amity School of Engineering and Technology, New Delhi 110061, IndiaUniversity School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, New Delhi 110078, IndiaDepartment of Mathematics, Amity School of Engineering and Technology, New Delhi 110061, IndiaCenter 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, NigeriaDepartment of Software Engineering, Kaunas University of Technology, Kaunas 44249, LithuaniaDepartment of Software Engineering, Kaunas University of Technology, Kaunas 44249, LithuaniaThe 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.http://www.mdpi.com/1099-4300/20/5/372software design defectssoftware qualitycode smellentropystatistical modelregression |
| spellingShingle | Aakanshi Gupta Bharti Suri Vijay Kumar Sanjay Misra Tomas Blažauskas Robertas Damaševičius Software Code Smell Prediction Model Using Shannon, Rényi and Tsallis Entropies Entropy software design defects software quality code smell entropy statistical model regression |
| title | Software Code Smell Prediction Model Using Shannon, Rényi and Tsallis Entropies |
| title_full | Software Code Smell Prediction Model Using Shannon, Rényi and Tsallis Entropies |
| title_fullStr | Software Code Smell Prediction Model Using Shannon, Rényi and Tsallis Entropies |
| title_full_unstemmed | Software Code Smell Prediction Model Using Shannon, Rényi and Tsallis Entropies |
| title_short | Software Code Smell Prediction Model Using Shannon, Rényi and Tsallis Entropies |
| title_sort | software code smell prediction model using shannon renyi and tsallis entropies |
| topic | software design defects software quality code smell entropy statistical model regression |
| url | http://www.mdpi.com/1099-4300/20/5/372 |
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