RT book part
T1 A Comprehensive Review on Equivalent Mutant Detection Using Machine Learning
A1 Guerrero Contreras, Gabriel José
A1 Balderas Díaz, Sara
A1 Delgado Pérez, Pedro
A1 Medina Bulo, María Inmaculada
A2 Ingeniería Informática
K1 Equivalent mutants
K1 mutation testing
K1 machine learning
K1 neural networks
K1 software testing
AB Mutation testing evaluates test suite effectiveness by introducing small code modifications ("mutants") and checking if tests detect them. A major challenge is equivalent mutants, which are code changes that do not alter program behavior, making detection computationally expensive and undecidable. This review examines Machine Learning approaches, including Abstract Syntax Tree Neural Networks and Tree-Based Convolutional Neural Networks, to address this issue. Key challenges include limited datasets, inconsistent metrics, and reproducibility concerns. The study highlights the need for standardized frameworks and future research directions in mutation testing.
PB Springer
SN 978-3-031-92473-6
YR 2025
FD 2025-05-16
LK http://hdl.handle.net/10498/37666
UL http://hdl.handle.net/10498/37666
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 06-nov-2026