Bug Severity Assessment in Cross Project Context and Identifying Training Candidates
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[1] M. F. Porter,et al. An algorithm for suffix stripping , 1997 .
[2] Thomas Zimmermann,et al. What Makes a Good Bug Report? , 2008, IEEE Transactions on Software Engineering.
[3] Jongmoon Baik,et al. Value-cognitive boosting with a support vector machine for cross-project defect prediction , 2014, Empirical Software Engineering.
[4] Punam Bedi,et al. An empirical evaluation of cross project priority prediction , 2014, International Journal of System Assurance Engineering and Management.
[5] David Lo,et al. On the unreliability of bug severity data , 2016, Empirical Software Engineering.
[6] Akiko Aizawa,et al. An information-theoretic perspective of tf-idf measures , 2003, Inf. Process. Manag..
[7] TurhanBurak,et al. Data mining source code for locating software bugs , 2009 .
[8] Guangchun Luo,et al. Transfer learning for cross-company software defect prediction , 2012, Inf. Softw. Technol..
[9] Chih-Jen Lin,et al. A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.
[10] 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..
[11] Ayse Basar Bener,et al. On the relative value of cross-company and within-company data for defect prediction , 2009, Empirical Software Engineering.
[12] David Lo,et al. Automated prediction of bug report priority using multi-factor analysis , 2014, Empirical Software Engineering.
[13] Ye Yang,et al. An investigation on the feasibility of cross-project defect prediction , 2012, Automated Software Engineering.
[14] Andrew K. C. Wong,et al. Classification of Imbalanced Data: a Review , 2009, Int. J. Pattern Recognit. Artif. Intell..
[15] Ayse Basar Bener,et al. Data mining source code for locating software bugs: A case study in telecommunication industry , 2009, Expert Syst. Appl..