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Expand Up @@ -42,6 +42,7 @@ https://arxiv.org/pdf/2402.06916
* Tecimer, K. A., Tüzün, E., Dibeklioglu, H., & Erdogmus, H. (2021, June). Detection and elimination of systematic labeling bias in code reviewer recommendation systems. In _Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering_ (pp. 181-190). https://www.researchgate.net/profile/Kazim-Tecimer/publication/352106199_Detection_and_Elimination_of_Systematic_Labeling_Bias_in_Code_Reviewer_Recommendation_Systems/links/60b935c9a6fdcc22ead3bcfa/Detection-and-Elimination-of-Systematic-Labeling-Bias-in-Code-Reviewer-Recommendation-Systems.pdf
* Biazotto, J. P., Feitosa, D., Avgeriou, P., & Nakagawa, E. Y. (2024). Technical debt management automation: State of the art and future perspectives. Information and Software Technology, 167(107375), 107375. [doi:10.1016/j.infsof.2023.107375](https://doi.org/10.1016/j.infsof.2023.107375)
* Buchner, S., & Riehle, D. (2023). The Business Impact of Inner Source and How to Quantify It. In _ACM Computing Surveys_ (Vol. 56, Issue 2, pp. 1–27). Association for Computing Machinery (ACM). https://doi.org/10.1145/3611648
* Snell, M. (2023). Open Source Software Badging: The Proof Is in the Project. University of Nebraska at Omaha. [https://www.proquest.com/openview/009271a373ed9c9d5267f496a522529e/](https://www.proquest.com/openview/009271a373ed9c9d5267f496a522529e/)

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