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Ali Reshi, Junaid, Singh, Satwinder. (1397). Investigating the Role of Code Smells in Preventive Maintenance. , 10(4), 41-63. doi: 10.22059/jitm.2019.274968.2335
Junaid Ali Reshi; Satwinder Singh. "Investigating the Role of Code Smells in Preventive Maintenance". , 10, 4, 1397, 41-63. doi: 10.22059/jitm.2019.274968.2335
Ali Reshi, Junaid, Singh, Satwinder. (1397). 'Investigating the Role of Code Smells in Preventive Maintenance', , 10(4), pp. 41-63. doi: 10.22059/jitm.2019.274968.2335
Ali Reshi, Junaid, Singh, Satwinder. Investigating the Role of Code Smells in Preventive Maintenance. , 1397; 10(4): 41-63. doi: 10.22059/jitm.2019.274968.2335

Investigating the Role of Code Smells in Preventive Maintenance

Journal of Information Technology Management
مقاله 4، دوره 10، شماره 4، 2018، صفحه 41-63    اصل مقاله (1.14 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jitm.2019.274968.2335
نویسندگان
Junaid Ali Reshi* 1؛ Satwinder Singh2
1, M.Tech Student, Department of Computer Science and Technology, Central University of Punjab, Bhatinda, Punjab, India.
2Assistant Prof., Department of Computer Science and Technology, Central University of Punjab, Bhatinda, Punjab, India.
چکیده
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.
کلیدواژه‌ها
Preventive maintenance؛ Code smells؛ Machine learning؛ Random forest
آمار
تعداد مشاهده مقاله: 887
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