Open AccessMulticlass Software Bug Severity Classification using Decision Tree, Naive Bayes and Bagging
Author(s) -
Raj Kumar
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i2.1524
Subject(s) - naive bayes classifier , decision tree , computer science , software bug , machine learning , tree (set theory) , software , artificial intelligence , data mining , mathematics , support vector machine , operating system , mathematical analysis
The software applications are experiencing the challenges of ever-growing complexity caused by the increase in the number of bugs. The software development process has been adversely affected due to the wastage of resources caused due to the bugs. It is imperative to identify and predict bugs to facilitate the software development process. Software bugs can be classified according to the severity of the bugs. In this paper a comparative analysis of Decision Tree, Naïve Bayes and Bagging approach is done for the bug severity classification. A comparative analysis of the Naïve Bayes, Decision Tree and Bagging approach is done for the accuracy, precision, recall and F-measure parameters