Predicting the Severity of Open Source Bug Reports Using Unsupervised and Supervised Techniques

[1]  Tim Menzies,et al.  Automated severity assessment of software defect reports , 2008, 2008 IEEE International Conference on Software Maintenance.

[2]  David Lo,et al.  Information Retrieval Based Nearest Neighbor Classification for Fine-Grained Bug Severity Prediction , 2012, 2012 19th Working Conference on Reverse Engineering.

[3]  David Lo,et al.  On the unreliability of bug severity data , 2016, Empirical Software Engineering.

[4]  Thomas Zimmermann,et al.  What Makes a Good Bug Report? , 2010, IEEE Trans. Software Eng..

[5]  Rong Chen,et al.  Using Knowledge Transfer and Rough Set to Predict the Severity of Android Test Reports via Text Mining , 2017, Symmetry.

[6]  Bart Goethals,et al.  Predicting the severity of a reported bug , 2010, 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010).

[7]  K. K. Chaturvedi,et al.  An Empirical Comparison of Machine Learning Techniques in Predicting the Bug Severity of Open and Closed Source Projects , 2012, Int. J. Open Source Softw. Process..

[8]  Per Runeson,et al.  Detection of Duplicate Defect Reports Using Natural Language Processing , 2007, 29th International Conference on Software Engineering (ICSE'07).

[9]  M Mrunalini,et al.  Predicting the severity of bug reports using classification algorithms , 2016, 2016 International Conference on Circuits, Controls, Communications and Computing (I4C).

[10]  Serge Demeyer,et al.  Comparing Mining Algorithms for Predicting the Severity of a Reported Bug , 2011, 2011 15th European Conference on Software Maintenance and Reengineering.

[11]  K. K. Chaturvedi,et al.  Determining Bug severity using machine learning techniques , 2012, 2012 CSI Sixth International Conference on Software Engineering (CONSEG).