Investigating the Role of Code Smells in Preventive Maintenance
The quest for improving the software quality has given rise to various studies which focus on the enhancement of the quality of software through various processes. Code smells, which are indicators of the software quality have not been put to an extensive study for as to determine their role in the...
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doaj-a2671c86d7bf42189c4df5c3d51eae2b2020-11-25T01:23:26ZfasUniversity of TehranJournal of Information Technology Management 2008-58932423-50592019-01-01104416310.22059/jitm.2019.274968.233572760Investigating the Role of Code Smells in Preventive MaintenanceJunaid Ali Reshi0Satwinder Singh1PhD Candidate, Department of Computer Science and Technology, Central University of Punjab, Bhatinda, Punjab, India.Assistant Prof., Department of Computer Science and Technology, Central University of Punjab, Bhatinda, Punjab, India.The quest for improving the software quality has given rise to various studies which focus on the enhancement of the quality of software through various processes. Code smells, which are indicators of the software quality have not been put to an extensive study for as to determine their role in the prediction of defects in the software. This study aims to investigate the role of code smells in prediction of non-faulty classes. We examine the Eclipse software with four versions (3.2, 3.3, 3.6, and 3.7) for metrics and smells. Further, different code smells, derived subjectively through iPlasma, are taken into conjugation and three efficient, but subjective models are developed to detect code smells on each of Random Forest, J48 and SVM machine learning algorithms. This model is then used to detect the absence of defects in the four Eclipse versions. The effect of balanced and unbalanced datasets is also examined for these four versions. The results suggest that the code smells can be a valuable feature in discriminating absence of defects in a software.https://jitm.ut.ac.ir/article_72760_91519e1807204617d31463e6ac2d4cc2.pdfPreventive maintenanceCode smellsMachine learningRandom forest |
collection |
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language |
fas |
format |
Article |
sources |
DOAJ |
author |
Junaid Ali Reshi Satwinder Singh |
spellingShingle |
Junaid Ali Reshi Satwinder Singh Investigating the Role of Code Smells in Preventive Maintenance Journal of Information Technology Management Preventive maintenance Code smells Machine learning Random forest |
author_facet |
Junaid Ali Reshi Satwinder Singh |
author_sort |
Junaid Ali Reshi |
title |
Investigating the Role of Code Smells in Preventive Maintenance |
title_short |
Investigating the Role of Code Smells in Preventive Maintenance |
title_full |
Investigating the Role of Code Smells in Preventive Maintenance |
title_fullStr |
Investigating the Role of Code Smells in Preventive Maintenance |
title_full_unstemmed |
Investigating the Role of Code Smells in Preventive Maintenance |
title_sort |
investigating the role of code smells in preventive maintenance |
publisher |
University of Tehran |
series |
Journal of Information Technology Management |
issn |
2008-5893 2423-5059 |
publishDate |
2019-01-01 |
description |
The quest for improving the software quality has given rise to various studies which focus on the enhancement of the quality of software through various processes. Code smells, which are indicators of the software quality have not been put to an extensive study for as to determine their role in the prediction of defects in the software. This study aims to investigate the role of code smells in prediction of non-faulty classes. We examine the Eclipse software with four versions (3.2, 3.3, 3.6, and 3.7) for metrics and smells. Further, different code smells, derived subjectively through iPlasma, are taken into conjugation and three efficient, but subjective models are developed to detect code smells on each of Random Forest, J48 and SVM machine learning algorithms. This model is then used to detect the absence of defects in the four Eclipse versions. The effect of balanced and unbalanced datasets is also examined for these four versions. The results suggest that the code smells can be a valuable feature in discriminating absence of defects in a software. |
topic |
Preventive maintenance Code smells Machine learning Random forest |
url |
https://jitm.ut.ac.ir/article_72760_91519e1807204617d31463e6ac2d4cc2.pdf |
work_keys_str_mv |
AT junaidalireshi investigatingtheroleofcodesmellsinpreventivemaintenance AT satwindersingh investigatingtheroleofcodesmellsinpreventivemaintenance |
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