Predicting Bug Priority Using Topic Modelling in Imbalanced Learning Environments

Jayalath Bandara Ekanayake
Additional contact information
Jayalath Bandara Ekanayake: Uva Wellassa University, Sri Lanka

International Journal of Systems and Service-Oriented Engineering (IJSSOE), 2021, vol. 11, issue 1, 31-42

Abstract: Manual classification of bug reports is time-consuming as the reports are received in large quantities. Alternatively, this project proposed automatic bug prediction models to classify the bug reports. The topics or the candidate keywords are mined from the developer description in bug reports using RAKE algorithm and converted into attributes. These attributes together with the target attribute—priority level—construct the training datasets. Naïve Bayes, logistic regression, and decision tree learner algorithms are trained, and the prediction quality was measured using area under recursive operative characteristics curves (AUC) as AUC does not consider the biasness in datasets. The logistics regression model outperforms the other two models providing the accuracy of 0.86 AUC whereas the naïve Bayes and the decision tree learner recorded 0.79 AUC and 0.81 AUC, respectively. The bugs can be classified without developer involvement and logistic regression is also a potential candidate as naïve Bayes for bug classification.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 18/IJSSOE.2021010103 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jssoe0:v:11:y:2021:i:1:p:31-42

Access Statistics for this article

International Journal of Systems and Service-Oriented Engineering (IJSSOE) is currently edited by Wuhui Chen

More articles in International Journal of Systems and Service-Oriented Engineering (IJSSOE) from IGI Global
Bibliographic data for series maintained by Journal Editor ().