Monday 10 March 2025
Deep learning has come a long way in recent years, with applications ranging from image and speech recognition to self-driving cars and medical diagnosis. But despite its many successes, deep learning is still prone to errors and faults that can have serious consequences.
To address this issue, researchers have been working on developing more robust testing methods for deep learning models. One approach has been mutation testing, which involves deliberately introducing errors into a model’s code or data to see how it responds. But traditional mutation testing techniques have some significant limitations – they often generate mutants that are too easy or too hard to detect, and may not accurately reflect the types of faults that occur in real-world systems.
A new paper published in the Journal of LaTeX Class Files introduces a novel approach to mutation testing for deep learning models called MUFF. The authors propose using automated stability checks and two new mutation operators – Weight Inhibitor and Neuron Inhibitor – to generate mutants that are more realistic and challenging to detect.
The MUFF system works by first generating mutants using traditional mutation operators, such as deleting neurons or changing weight values. It then uses an automated stability check to determine whether each mutant is stable and killable – in other words, whether it can be reliably detected as faulty. If a mutant is deemed unstable or non-killable, MUFF discards it and generates a new one.
The two new mutation operators introduced by the authors are designed to provide finer-grained control over the types of faults that are injected into the model. The Weight Inhibitor operator reduces the magnitude of certain weights in the model’s neural networks, while the Neuron Inhibitor operator temporarily disables specific neurons.
In an empirical evaluation, the authors compared MUFF to two other mutation testing tools for deep learning models – DEEPMUTATION++ and DEEPCRIME. They found that MUFF generated mutants with significantly higher sensitivity than the other two tools, and was also more efficient in terms of computational resources required.
The authors suggest that MUFF has important implications for the development and deployment of deep learning models in real-world applications. By providing a more realistic and challenging testing environment, MUFF can help developers identify and fix errors more effectively, reducing the risk of faulty models being deployed into production.
Overall, the introduction of MUFF represents an important advance in the field of mutation testing for deep learning models.
Cite this article: “Introducing MUFF: A Novel Approach to Mutation Testing for Deep Learning Models”, The Science Archive, 2025.
Deep Learning, Mutation Testing, Muff, Deep Learning Models, Testing Methods, Neural Networks, Weight Inhibitor, Neuron Inhibitor, Automated Stability Checks, Fault Detection
