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	When Simple is Better than Complex: Coverage and Mutation for DL Testing

Thursday

26.09

USI East Campus, Room D1.13

16:30 - 17:30


	Jinhan Kim Università della Svizzera italiana

Abstract: In the context of traditional software testing, coverage testing has served as a foundational step for mutation testing, implying that the latter builds upon the insights gained from coverage analysis. This trade-off is well-known: coverage testing offers a rapid and cost-effective assessment, whereas mutation testing, despite its higher cost, excels in quantifying the effectiveness of test sets. Interestingly, this paradigm appears to extend to the testing of deep learning systems, where coverage techniques provide a straightforward yet efficient evaluation, and mutation testing, though costly, delivers more comprehensive insights. Despite the plethora of proposed coverage criteria and mutation methods, a comparative analysis between these approaches remains unexplored. This raises the question: Given that mutation testing has traditionally been regarded as a more robust testing methodology compared to coverage testing, can it maintain its superiority in the realm of deep learning? In this talk, I will introduce and discuss several coverage and mutation methods with the aim of empirically exploring and addressing these questions.

Biography: Jinhan Kim is currently a postdoc at USI, working with Prof. Paolo Tonella. He completed his Ph.D. degree from KAIST under the supervision of Dr. Shin Yoo, where he focused on software engineering research, particularly on mutation testing, fault localization, and deep learning system testing.