# What Are We Really Testing in Mutation Testing for Machine Learning? A Critical Reflection

arXiv: https://arxiv.org/abs/2103.01341 · HTML: https://mlsanity.com/paper/2103.01341/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2021.

## Built on by (most cited first, 7 of 7)

- [Perturbation Augmentation for Fairer NLP](https://mlsanity.com/paper/2205.12586/) (2022)
- [Aries: Efficient Testing of Deep Neural Networks via Labeling-Free Accuracy Estimation](https://mlsanity.com/paper/2207.10942/) (2022)
- [A Probabilistic Framework for Mutation Testing in Deep Neural Networks](https://mlsanity.com/paper/2208.06018/) (2022)
- [D-Score: A White-Box Diagnosis Score for CNNs Based on Mutation Operators](https://mlsanity.com/paper/2304.00697/) (2023)
- [Quality Assurance in MLOps Setting: An Industrial Perspective](https://mlsanity.com/paper/2211.12706/) (2022)
- [Revising the Problem of Partial Labels from the Perspective of CNNs' Robustness](https://mlsanity.com/paper/2407.17630/) (2024)
- [Scalability and Maintainability Challenges and Solutions in Machine Learning: Systematic Literature Review](https://mlsanity.com/paper/2504.11079/) (2025)

## Abstract (arXiv)

Mutation testing is a well-established technique for assessing a test suite's quality by injecting artificial faults into production code. In recent years, mutation testing has been extended to machine learning (ML) systems, and deep learning (DL) in particular; researchers have proposed approaches, tools, and statistically sound heuristics to determine whether mutants in DL systems are killed or not. However, as we will argue in this work, questions can be raised to what extent currently used mutation testing techniques in DL are actually in line with the classical interpretation of mutation testing. We observe that ML model development resembles a test-driven development (TDD) process, in which a training algorithm (`programmer') generates a model (program) that fits the data points (test data) to labels (implicit assertions), up to a certain threshold. However, considering proposed mutation testing techniques for ML systems under this TDD metaphor, in current approaches, the distinction between production and test code is blurry, and the realism of mutation operators can be challenged. We also consider the fundamental hypotheses underlying classical mutation testing: the competent programmer hypothesis and coupling effect hypothesis. As we will illustrate, these hypotheses do not trivially translate to ML system development, and more conscious and explicit scoping and concept mapping will be needed to truly draw parallels. Based on our observations, we propose several action points for better alignment of mutation testing techniques for ML with paradigms and vocabularies of classical mutation testing.