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Effects of Explicit Feature Traceability on Program Comprehension., , , , and . SE, volume P-300 of LNI, page 79-80. Gesellschaft für Informatik e.V., (2020)Empirical Analysis of Hidden Technical Debt Patterns in Machine Learning Software., and . PROFES, volume 11915 of Lecture Notes in Computer Science, page 195-202. Springer, (2019)Influence of confirmation biases of developers on software quality: an empirical study., and . Softw. Qual. J., 21 (2): 377-416 (2013)The Impact of Confirmation Bias on the Release-based Defect Prediction of Developer Groups., and . SEKE, page 461-466. Knowledge Systems Institute Graduate School, (2013)SZZ in the time of Pull Requests., , , , , , , , and . CoRR, (2022)First come first served: the impact of file position on code review., , , , and . ESEC/SIGSOFT FSE, page 483-494. ACM, (2022)Why Don't Developers Detect Improper Input Validation? '; DROP TABLE Papers; -., , , and . ICSE, page 499-511. IEEE, (2021)Less is More: Supporting Developers in Vulnerability Detection during Code Review., , , and . ICSE, page 1317-1329. ACM, (2022)Primers or reminders?: the effects of existing review comments on code review., , and . ICSE, page 1171-1182. ACM, (2020)Effects of explicit feature traceability on program comprehension., , , , and . ESEC/SIGSOFT FSE, page 338-349. ACM, (2019)