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Bug triaging Based on Tossing Sequence Modeling

Journal of Computer Science & Technology 2019年第5期34卷 942-956页

作者： Sheng-Qu Xi Yuan Yao Xu-Sheng Xiao Feng Xu Jian Lv State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Department of Electrical Engineering and Computer Science Case Western Reserve UniversityCleveland Ohio 44106-7071U.S.A.

Bug triaging, which routes the bug reports to potential fixers, is an integral step in software development and maintenance. To make bug triaging more efficient, many researchers propose to adopt machine learning and information retrieval techniques to identify some suitable fixers for a given bug report. However, none of the existing proposals simultaneously take into account the following three aspects that matter for the efficiency of bug triaging: 1) the textual content in the bug reports, 2) the metadata in the bug reports, and 3) the tossing sequence of the bug reports. To simultaneously make use of the above three aspects, we propose iTriage which first adopts a sequence-to-sequence model to jointly learn the features of textual content and tossing sequence, and then uses a classification model to integrate the features from textual content, metadata, and tossing sequence. Evaluation results on three different open-source projects show that the proposed approach has significantly improved the accuracy of bug triaging compared with the state-of-the-art approaches.

关键词： bug triaging tossing sequence software repository mining

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Bug Prioritization Using Average One Dependence Estimator

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Intelligent Automation & Soft Computing 2023年第6期36卷 3517-3533页

作者： Kashif Saleem Rashid Naseem Khalil Khan Siraj Muhammad Ikram Syed Jaehyuk Choi Department of IT and Computer Science Institute of Applied Sciences and TechnologyPakAustriaFochhshuleHaripurPakistan Faculty of Computer Sciences and Information Technology Superior UniversityLahore54660Pakistan Department of Computer Science Shaheed Benazir Bhutto UniversitySheringalUpper DirKhyber PakhtunkhwaPakitan School of Computing Gachon University1342Seongnam-daeroSujeong-guSeongnam-si13120Korea

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.

关键词： Bug report triaging prioritization support vector machine Naive Bayes

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Role of serial multiparametric magnetic resonance imaging in prostate cancer active surveillance

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World Journal of Radiology 2016年第4期8卷 410-418页

作者： Larissa J Vos Michele Janoski Keith Wachowicz Atiyah Yahya Oleksandr Boychak John Amanie Nadeem Pervez Matthew B Parliament Edith Pituskin B Gino Fallone Nawaid Usmani Department of Oncology Cross Cancer InstituteUniversity of Alberta Department of Radiology and Diagnostic Imaging Cross Cancer InstituteUniversity of Alberta

AIM:To examine whether addition of 3T multiparametric magnetic resonance imaging(mp MRI)to an active surveillance protocol could detect aggressive or progressive prostate ***:Twenty-three patients with low risk disease were enrolled on this active surveillance study,all of which had Gleason score 6 or less *** patients had clinical assessments,including digital rectal examination and prostate specific antigen(PSA)testing,every 6 mo with annual 3T mp MRI scans with gadolinium contrast and minimum sextant prostate *** MRI images were anonymized of patient identifiers and clinical information and each scan underwentradiological review without the other results *** statistics for demographics and follow-up as well as the sensitivity and specificity of mp MRI to identify prostate cancer and progressive disease were ***:During follow-up(median 24.8 mo)11 of 23 patients with low-risk prostate cancer had disease progression and were taken off study to receive definitive *** progression was identified through upstaging of Gleason score on subsequent biopsies for all 11 patients with only 2 patients also having a PSA doubling time of less than 2 *** 23 patients had biopsy confirmed prostate cancer but only 10 had a positive index of suspicion on mp MRI scans at baseline(43.5% sensitivity).Aggressive disease prediction from baseline mpM RI scans had satisfactory specificity(81.8%)but low sensitivity(58.3%).Twentytwo patients had serial mp MRI scans and evidence of disease progression was seen for 3 patients all of whom had upstaging of Gleason score on biopsy(30% specificity and 100% sensitivity).CONCLUSION:Addition of mp MRI imaging in active surveillance decision making may help in identifying aggressive disease amongst men with indolent prostate cancer earlier than traditional methods.

关键词： Active surveillance Treatment triaging Magnetic resonance imaging Indolent disease Prostate cancer

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