Bug Prioritization Using Average One Dependence Estimator

作     者：Kashif Saleem Rashid Naseem Khalil Khan Siraj Muhammad Ikram Syed Jaehyuk Choi 

作者机构：Department of IT and Computer ScienceInstitute of Applied Sciences and TechnologyPakAustriaFochhshuleHaripurPakistan Faculty of Computer Sciences and Information TechnologySuperior UniversityLahore54660Pakistan Department of Computer ScienceShaheed Benazir Bhutto UniversitySheringalUpper DirKhyber PakhtunkhwaPakitan School of ComputingGachon University1342Seongnam-daeroSujeong-guSeongnam-si13120Korea 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第36卷第6期

页      面：3517-3533页

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported in part by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF-2020R1A2C1013308) 

主　　题：Bug report triaging prioritization support vector machine Naive Bayes 

摘      要：Automation software need to be continuously updated by addressing software bugs contained in their ***,bugs have different levels of importance;hence,it is essential to prioritize bug reports based on their sever-ity and *** managing the deluge of incoming bug reports faces time and resource constraints from the development team and delays the resolu-tion of critical ***,bug report prioritization is *** study pro-poses 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 *** more the number of attributes,the more the *** proposed model is evaluated using precision,recall,F1-Score,accuracy,G-Measure,and Matthew’s correlation *** of the proposed model are compared with those of the support vector machine(SVM)and Naive Bayes(NB)*** and Mozilla datasetswere used as the sources of bug *** proposed model improved the bug repository management and out-performed the SVM and NB ***,the proposed model used a weaker attribute independence supposition than the former models,thereby improving prediction accuracy with minimal computational cost.