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Dataset of Real Faults in Deep Learning Systems., , , , , and . (February 2020)Replication Package: DeepAtash-LR: Focused Test Generation for Autonomous Driving Systems., , and . (December 2023)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)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)Taxonomy of real faults in deep learning systems., , , , , and . ICSE, page 1110-1121. ACM, (2020)When and Why Test Generators for Deep Learning Produce Invalid Inputs: an Empirical Study., and . ICSE, page 1161-1173. IEEE, (2023)Is this the lifecycle we really want?: an automated black-box testing approach for Android activities., , and . ISSTA/ECOOP Workshops, page 68-77. ACM, (2018)Engineering Self-adaptive Microservice Applications: An Experience Report., , , , and . ICSOC (1), volume 14419 of Lecture Notes in Computer Science, page 227-242. Springer, (2023)Deep Learning System Boundary Testing through Latent Space Style Mixing., , , and . CoRR, (2024)