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MAAT: a novel ensemble approach to addressing fairness and performance bugs for machine learning software.

, , , and . ESEC/SIGSOFT FSE, page 1122-1134. ACM, (2022)

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Characterizing and Detecting WebAssembly Runtime Bugs., , , , , , , , and . CoRR, (2023)SEntiMoji: an emoji-powered learning approach for sentiment analysis in software engineering., , , , and . ESEC/SIGSOFT FSE, page 841-852. ACM, (2019)MAAT: a novel ensemble approach to addressing fairness and performance bugs for machine learning software., , , and . ESEC/SIGSOFT FSE, page 1122-1134. ACM, (2022)AutoML from Software Engineering Perspective: Landscapes and Challenges., , and . MSR, page 39-51. IEEE, (2023)Fairness Testing of Machine Translation Systems., , , and . ACM Trans. Softw. Eng. Methodol., 33 (6): 156 (July 2024)Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification (Extended Abstract)., , , , , and . IJCAI, page 4701-4705. ijcai.org, (2020)Sister Conferences Best Papers.A comprehensive study on challenges in deploying deep learning based software., , , , , and . ESEC/SIGSOFT FSE, page 750-762. ACM, (2020)Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data., , , , , , and . WWW, page 935-946. ACM / IW3C2, (2021)Who Judges the Judge: An Empirical Study on Online Judge Tests., , , , , , , and . ISSTA, page 334-346. ACM, (2023)An Empirical Study on Deployment Faults of Deep Learning Based Mobile Applications., , , , , , and . ICSE, page 674-685. IEEE, (2021)