Title: Automated labelling and severity prediction of software bug reports

Authors: Ahmed Fawzi Otoom; Doaa Al-Shdaifat; Maen Hammad; Emad E. Abdallah; Ashraf Aljammal

Addresses: Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology, The Hashemite University, Zarqa, Jordan ' Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology, The Hashemite University, Zarqa, Jordan ' Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology, The Hashemite University, Zarqa, Jordan ' Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology, The Hashemite University, Zarqa, Jordan ' Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology, The Hashemite University, Zarqa, Jordan

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.

Keywords: severity prediction; software bugs; machine learning; bug labelling.

DOI: 10.1504/IJCSE.2019.101343

International Journal of Computational Science and Engineering, 2019 Vol.19 No.3, pp.334 - 342

Received: 01 Jul 2016
Accepted: 03 Feb 2017
Published online: 05 Aug 2019 *

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