Automated severity assessment of software defect reports

By Tim Menzies, Andrian Marcus. 2008 IEEE International Conference on Software Maintenance, 2008

328 citations recorded by the source corpus.

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Comparing and experimenting machine learning techniques for code smell detection (2015, background): “Several authors have recently worked on the prediction of defect severity by means of machine learning techniques (Menzies and Marcus 2008) (Lamkanfi and Demeyer 2010) (Tian et al.”
RCLinker: Automated Linking of Issue Reports and Commits Leveraging Rich Contextual Information (2015, unknown): “made use of various classification algorithms to predict the values of the severity fields of bug reports [42], [31], [32], [57].”
What Makes a Good Bug Report? (2008, background): “Several studies used bug reports to automatically assign developers to bug reports [4], [5], [20], assign locations to bug reports [19], track features over time [26], recognize bug duplicates [22], [29], [32], [54], [60], assess the severity [44], predict effort for bug reports [63], identify bug t”
Predicting the severity of a reported bug (2010, methodology): “Menzies et al. predict the severity based on a rule learning technique which also uses the textual descriptions of reported bugs [16].”
A discriminative model approach for accurate duplicate bug report retrieval (2010, background): “Menzies and Marcus also suggested a classiﬁcation based approach to predicting the severity of bug reports [14].”
Towards more accurate retrieval of duplicate bug reports (2011, background): “A study on predicting the severity of bug reports is performed by Menzies and Marcus [19].”