Automated labelling and severity prediction of software bug reports Online publication date: Mon, 05-Aug-2019
by Ahmed Fawzi Otoom; Doaa Al-Shdaifat; Maen Hammad; Emad E. Abdallah; Ashraf Aljammal
International Journal of Computational Science and Engineering (IJCSE), Vol. 19, No. 3, 2019
Abstract: Our main aim is to develop an intelligent classifier that is capable of predicting the severity and label (type) of a newly submitted bug report through a bug tracking system. For this purpose, we build two datasets that are based on 350 bug reports from the open-source community (Eclipse, Mozilla, and Gnome). These datasets are characterised with various textual features. Based on this information, we train variety of discriminative models that are used for automated labelling and severity prediction of a newly submitted bug report. A boosting algorithm is also implemented for an enhanced performance. The classification performance is measured using accuracy and a set of other measures. For automated labelling, the accuracy reaches around 91% with the AdaBoost algorithm and cross validation test. On the other hand, for severity prediction, the classification accuracy reaches around 67% with the AdaBoost algorithm and cross validation test. Overall, the results are encouraging.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Science and Engineering (IJCSE):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook