Open Access

ARTICLE

Bug Prioritization Using Average One Dependence Estimator

Kashif Saleem¹, Rashid Naseem¹, Khalil Khan^1,2, Siraj Muhammad³, Ikram Syed^4,*, Jaehyuk Choi^4,*

1 Department of IT and Computer Science, Institute of Applied Sciences and Technology, PakAustriaFochhshule, Haripur, Pakistan
2 Faculty of Computer Sciences and Information Technology, Superior University, Lahore, 54660, Pakistan
3 Department of Computer Science, Shaheed Benazir Bhutto University, Sheringal, Upper Dir, Khyber Pakhtunkhwa, Pakitan
4 School of Computing, Gachon University, 1342, Seongnam-daero, Sujeong-gu, Seongnam-si, 13120, Korea

* Corresponding Authors: Ikram Syed. Email: ; Jaehyuk Choi. Email:

Intelligent Automation & Soft Computing 2023, 36(3), 3517-3533. https://doi.org/10.32604/iasc.2023.036356

Received 27 September 2022; Accepted 06 December 2022; Issue published 15 March 2023

Abstract

Automation software need to be continuously updated by addressing software bugs contained in their repositories. However, bugs have different levels of importance; hence, it is essential to prioritize bug reports based on their severity and importance. Manually managing the deluge of incoming bug reports faces time and resource constraints from the development team and delays the resolution of critical bugs. Therefore, bug report prioritization is vital. This study proposes a new model for bug prioritization based on average one dependence estimator; it prioritizes bug reports based on severity, which is determined by the number of attributes. The more the number of attributes, the more the severity. The proposed model is evaluated using precision, recall, F1-Score, accuracy, G-Measure, and Matthew’s correlation coefficient. Results of the proposed model are compared with those of the support vector machine (SVM) and Naive Bayes (NB) models. Eclipse and Mozilla datasetswere used as the sources of bug reports. The proposed model improved the bug repository management and outperformed the SVM and NB models. Additionally, the proposed model used a weaker attribute independence supposition than the former models, thereby improving prediction accuracy with minimal computational cost.

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Keywords

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