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...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 sever-ity and importance.Manually managing the deluge of incoming bug reports faces time and resource constraints from the development team and delays the resolu-tion of critical bugs.Therefore,bug report prioritization is vital.This 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 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 out-performed 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.展开更多
A rapidly growing number of successful genome sequencing projects in plant pathogenic fungi greatly increase the demands for tools and methodologies to study fungal pathogenicity at genomic scale. Magnaporthe oryzae i...A rapidly growing number of successful genome sequencing projects in plant pathogenic fungi greatly increase the demands for tools and methodologies to study fungal pathogenicity at genomic scale. Magnaporthe oryzae is an economically important plant pathogenic fungus whose genome is fully sequenced. Recently we have reported the development and application of functional genomics platform technologies in M. oryzae. This model approach would have many practical ramifications in design and implementation of upcoming functional genomics studies of filamentous fungi aimed at understanding fungal pathogenicity.展开更多
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).
文摘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 sever-ity and importance.Manually managing the deluge of incoming bug reports faces time and resource constraints from the development team and delays the resolu-tion of critical bugs.Therefore,bug report prioritization is vital.This 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 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 out-performed 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.
基金a grant from Biogreen 21 Project (No. 20080401034044)the Rural Development Administration of Korea, the Crop Functional Genomics Center (No. CG1141) of the 21st Century Frontier Research Program funded by the Ministry of Science and Technology of Koreathe Korean Research Foundation Grant (No. KRF-2006-005-J04701)
文摘A rapidly growing number of successful genome sequencing projects in plant pathogenic fungi greatly increase the demands for tools and methodologies to study fungal pathogenicity at genomic scale. Magnaporthe oryzae is an economically important plant pathogenic fungus whose genome is fully sequenced. Recently we have reported the development and application of functional genomics platform technologies in M. oryzae. This model approach would have many practical ramifications in design and implementation of upcoming functional genomics studies of filamentous fungi aimed at understanding fungal pathogenicity.
