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SBST Tool Competition 2022., , , and . SBST@ICSE, page 25-32. IEEE, (2022)Efficient and Effective Feature Space Exploration for Testing Deep Learning Systems., , , and . ACM Trans. Softw. Eng. Methodol., 32 (2): 49:1-49:38 (April 2023)Testing machine learning based systems: a systematic mapping., , , , , and . Empir. Softw. Eng., 25 (6): 5193-5254 (2020)When and Why Test Generators for Deep Learning Produce Invalid Inputs: an Empirical Study., and . CoRR, (2022)DeepHyperion: Exploring the Feature Space of Deep Learning-based Systems through Illumination Search., , , and . Software Engineering, volume P-332 of LNI, page 131-132. Gesellschaft für Informatik e.V., (2023)Engineering Self-adaptive Microservice Applications: An Experience Report., , , , and . ICSOC (1), volume 14419 of Lecture Notes in Computer Science, page 227-242. Springer, (2023)When and Why Test Generators for Deep Learning Produce Invalid Inputs: an Empirical Study., and . ICSE, page 1161-1173. IEEE, (2023)Taxonomy of real faults in deep learning systems., , , , , and . ICSE, page 1110-1121. ACM, (2020)An Empirical Study on Low- and High-Level Explanations of Deep Learning Misbehaviours., , and . ESEM, page 1-11. IEEE, (2023)DeepMetis: Augmenting a Deep Learning Test Set to Increase its Mutation Score., , , and . ASE, page 355-367. IEEE, (2021)