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Finding the best learning to rank algorithms for effort-aware defect prediction.

, , , , , , , and . Inf. Softw. Technol., (May 2023)

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A smart access control mechanism based on user preference in online social networks., , and . Concurr. Comput. Pract. Exp., (2023)G3MBench: A Graph-Based Benchmark Suite for Memory Management on Microcontrollers., , and . CIT, page 185-191. IEEE Computer Society, (2012)Checkpointing-aware Data Allocation for Energy Harvesting Powered Non-volatile Processors., , and . NVMSA, page 1-6. IEEE, (2019)On the relative value of imbalanced learning for code smell detection., , , , , and . Softw. Pract. Exp., 53 (10): 1902-1927 (October 2023)NVLH: Crash-Consistent Linear Hashing for Non-Volatile Memory., , , , , and . NVMSA, page 117-118. IEEE, (2018)Finding the best learning to rank algorithms for effort-aware defect prediction., , , , , , , and . Inf. Softw. Technol., (May 2023)Enhancing Deep Learning Vulnerability Detection through Imbalance Loss Functions: An Empirical Study., , , , , , and . Internetware, ACM, (2024)Autonomous Driving in Unstructured Environments: How Far Have We Come?, , , , , , , , , and 28 other author(s). CoRR, (2024)Predicting the precise number of software defects: Are we there yet?, , , , , and . Inf. Softw. Technol., (2022)Revisiting 'revisiting supervised methods for effort-aware cross-project defect prediction'., , , , , and . IET Softw., 17 (4): 472-495 (August 2023)