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arXiv · 2507.11199

New Formulation of DNN Statistical Mutation Killing for Ensuring Monotonicity: A Technical Report

Jinhan Kim·Nargiz Humbatova·Gunel Jahangirova·Shin Yoo·Paolo Tonella
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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.

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Jinhan Kim, Nargiz Humbatova, Gunel Jahangirova, Shin Yoo, Paolo Tonella. 2025-07-15. New Formulation of DNN Statistical Mutation Killing for Ensuring Monotonicity: A Technical Report. https://arxiv.org/abs/2507.11199

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