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Using Analytics to Quantify Interest of Self-Admitted Technical Debt., , , and . QuASoQ/TDA@APSEC, volume 1771 of CEUR Workshop Proceedings, page 68-71. CEUR-WS.org, (2016)Why do developers use trivial packages? an empirical case study on npm., , , , and . ESEC/SIGSOFT FSE, page 385-395. ACM, (2017)Prioritizing the devices to test your app on: a case study of Android game apps., , , and . SIGSOFT FSE, page 610-620. ACM, (2014)Bots for Pull Requests: The Good, the Bad, and the Promising., , , , , , and . ICSE, page 274-286. ACM, (2022)Class imbalance evolution and verification latency in just-in-time software defect prediction., , , and . ICSE, page 666-676. IEEE / ACM, (2019)Is using deep learning frameworks free?: characterizing technical debt in deep learning frameworks., , , , , and . ICSE-SEIS, page 1-10. ACM, (2020)Detecting and quantifying different types of self-admitted technical Debt., and . MTD@ICSME, page 9-15. IEEE Computer Society, (2015)Challenges in mobile apps: a multi-disciplinary perspective., , and . CASCON, page 378-381. IBM / ACM, (2013)Studying re-opened bugs in open source software., , , , , , , and . Empir. Softw. Eng., 18 (5): 1005-1042 (2013)Dependency Update Strategies and Package Characteristics., , , and . CoRR, (2023)