Work

Exploring the Fundamentals of Mutations in Deep Neural Networks

Year: 2024

Type: conference-paper

Abstract: The increasing popularity of deep neural networks (DNNs) has led to the adaptation of mutation analysis from classical software development to the machine learning (ML) paradigm. However, determining what to mutate and to what extent remains a challenge. To this aim, two questions are of importance: (i) which ML artifacts can be modified to generate acceptable mutations?, and (ii) what extent of change in a specific metric qualifies as a useful mutant?

Source: ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS)

DOI: 10.1145/3652620.3687426

Authors: Zaheed Ahmed, Philip Makedonski

Institution: University of Göttingen

Language: English

Topic: Software Testing and Debugging Techniques

Subfield: Software

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4403935779

FWCI: 0.277

Cites: 41

Cited by: 1

Related to: 10