{"ID":2894359,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.11199","arxiv_id":"2507.11199","title":"New Formulation of DNN Statistical Mutation Killing for Ensuring Monotonicity: A Technical Report","abstract":"Mutation testing has emerged as a powerful technique for evaluating the effectiveness of test suites for Deep Neural Networks. Among existing approaches, the statistical mutant killing criterion of DeepCrime has leveraged statistical testing to determine whether a mutant significantly differs from the original model. However, it suffers from a critical limitation: it violates the monotonicity property, meaning that expanding a test set may result in previously killed mutants no longer being classified as killed. In this technical report, we propose a new formulation of statistical mutant killing based on Fisher exact test that preserves the statistical rigour of it while ensuring monotonicity.","short_abstract":"Mutation testing has emerged as a powerful technique for evaluating the effectiveness of test suites for Deep Neural Networks. Among existing approaches, the statistical mutant killing criterion of DeepCrime has leveraged statistical testing to determine whether a mutant significantly differs from the original model. H...","url_abs":"https://arxiv.org/abs/2507.11199","url_pdf":"https://arxiv.org/pdf/2507.11199v1","authors":"[\"Jinhan Kim\",\"Nargiz Humbatova\",\"Gunel Jahangirova\",\"Shin Yoo\",\"Paolo Tonella\"]","published":"2025-07-15T11:12:08Z","proceeding":"cs.SE","tasks":"[\"cs.SE\"]","methods":"[]","has_code":false}