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In Software Development Life Cycle, fixing defect bugs is one of the essential activities of the software maintenance phase. Bug severity indicates how major or minor the bug impacts on the execution of the system and how rapidly the developer should fix it. Triaging a vast amount of new bugs submitted to the software bug repositories is a cumbersome and time-consuming process. Manual triage might lead to a mistake in assigning the appropriate severity level for each bug. As a consequence, a delay for fixing severe software bugs will take place. However, the whole process of assigning the severity level for bug reports should be automated. In this paper, we aim to build prediction models that will be utilized to determine the class of the severity (severe or non-severe) of the reported bug. To validate our approach, we have constructed a dataset from historical bug reports stored in JIRA bug tracking system. These bug reports are related to different closed-source projects developed by INTIX Company located in Amman, Jordan. We compare eight popular machine learning algorithms, namely Naive Bayes, Naive Bayes Multinomial, Support Vector Machine, Decision Tree (J48), Random Forest, Logistic Model Trees, Decision Rules (JRip) and K-Nearest Neighbor in terms of accuracy, F-measure and Area Under the Curve (AUC). According to the experimental results, a Decision Tree algorithm called Logistic Model Trees achieved better performance compared to other machine learning algorithms in terms of Accuracy, AUC and F-measure with values of 86.31, 0.90 and 0.91, respectively.

Machine Learning Approaches for Predicting the Severity Level of Software Bug Reports in Closed Source Projects