Automatic bug assignments without texts: a study

Graphical abstract

References

[1] Li Z, Zhong H. Revisiting textual feature of bug-triage approach. In: Proceedings of the 36th IEEE/ACM International Conference on Automated Software Engineering (ASE). 2021, 1183−1185

[2] Kohavi R, John G H . Wrappers for feature subset selection. Artificial Intelligence, 1997, 97( 1−2): 273–324

[3] Guyon I, Elisseeff A . An introduction to variable and feature selection. The Journal of Machine Learning Research, 2003, 3: 1157–1182

[4] Ahsan S N, Ferzund J, Wotawa F. Automatic software bug triage system (BTS) based on latent semantic indexing and support vector machine. In: Proceedings of the 4th International Conference on Software Engineering Advances. 2009, 216−221

[5] Banitaan S, Alenezi M. TRAM: an approach for assigning bug reports using their metadata. In: Proceedings of the 3rd International Conference on Communications and Information Technology (ICCIT). 2013, 215−219

[6] Jonsson L, Borg M, Broman D, Sandahl K, Eldh S, Runeson P . Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts. Empirical Software Engineering, 2016, 21( 4): 1533–1578

[7] Zhang W, Wang S, Yang Y, Wang Q. Heterogeneous network analysis of developer contribution in bug repositories. In: Proceedings of International Conference on Cloud and Service Computing. 2013, 98−105

[8] Sarkar A, Rigby P C, Bartalos B. Improving bug triaging with high confidence predictions at Ericsson. In: Proceedings of IEEE International Conference on Software Maintenance and Evolution (ICSME). 2019, 81−91

RIGHTS & PERMISSIONS

Higher Education Press