Evaluating Probabilistic Topic Models for Bug Triaging Tasks-Reference-Cited by-同舟云学术

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Evaluating Probabilistic Topic Models for Bug Triaging Tasks

Published:2023 Issue: Volume: Page:44-69
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
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Author:

Atzberger Daniel,Schneider Jonathan,Scheibel Willy,Trapp Matthias,Döllner Jürgen

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-36597-3_3

Reference45 articles.

1. Aktas, E.U., Yilmaz, C.: Automated issue assignment: results and insights from an industrial case. Empir. Softw. Eng. 25(5), 3544–3589 (2020). https://doi.org/10.1007/s10664-020-09846-3

2. Atzberger, D., Schneider, J., Scheibel, W., Limberger, D., Trapp, M., Döllner, J.: Mining developer expertise from bug tracking systems using the author-topic model. In: Proceedings of the 17th International Conference on Evaluation of Novel Approaches to Software Engineering, ENASE 2022, pp. 107–118. INSTICC, SciTePress (2022). https://doi.org/10.5220/0011045100003176

3. Banitaan, S., Alenezi, M.: TRAM: an approach for assigning bug reports using their metadata. In: Proceedings 3rd International Conference on Communications and Information Technology, ICCIT 2013, pp. 215–219. IEEE (2013). https://doi.org/10.1109/ICCITechnology.2013.6579552

4. Bhattacharya, P., Neamtiu, I.: Fine-grained incremental learning and multi-feature tossing graphs to improve bug triaging. In: Proceedings International Conference on Software Maintenance, ICSM 2010, pp. 1–10. IEEE (2010). https://doi.org/10.1109/ICSM.2010.5609736

5. Blei, D., Ng, A., Jordan, M.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

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1. Machine Learning Approaches in Contemporary Automatic Bug Triaging and Analysis of Research Gaps;2024 Sixth International Conference on Computational Intelligence and Communication Technologies (CCICT);2024-04-19

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