RT journal article
T1 Methodological pitfalls in predictive mutation testing: threats, impact and open challenges
A1 Delgado Pérez, Pedro
A1 Balderas Díaz, Sara
A1 Medina Bulo, María Inmaculada
A1 Guerrero Contreras, Gabriel José
A2 Ingeniería Informática
K1 Predictive Mutation Testing (PMT)
K1 Mutation testing
K1 Software testing
K1 Machine learning
AB Predictive Mutation Testing (PMT) has emerged as a promising technique for reducing the high computational cost of traditional mutation testing. This is achieved by using predictive models to estimate mutant behavior without actually requiring their execution. However, despite its growing interest, PMT is still a young and evolving field and, therefore, its experimental studies remain particularly vulnerable to various methodological threats that may compromise the validity, comparability, and reproducibility of results. This paper aims to identify and examine key methodological threats in prior PMT studies that not only affect their validity, but may also limit the technique’s ability to reach its full predictive potential. For each of the eight identified threats, spanning the main stages of the PMT workflow, we describe its nature, analyze how it has been addressed (or overlooked) in previous work, and offer a validation list of recommended practices. We further conduct an empirical validation to substantiate the impact of these threats. In addition, based on the gaps identified in this analysis, we outline several open challenges that remain unexplored in the field, such as the lack of standardization in dataset sharing, the prevalence of Java-centric studies with method-level operators, and the challenges posed by class imbalance and project heterogeneity. This work contributes to strengthening the methodological soundness of PMT research, promoting more meaningful cross-study comparisons, and encouraging the adoption of practices that foster reproducibility, scalability, and real-world applicability.
PB Springer
YR 2026
FD 2026-05-21
LK http://hdl.handle.net/10498/40330
UL http://hdl.handle.net/10498/40330
LA eng
NO Funding for open access publishing: Universidad de Cádiz/CBUA. This publication is part of the I+D+i grant PID2021-122215NB-C33 funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU, and by the Spanish Ministry of Science and Innovation under the Excellence Network AI4Software (Red2022-134647-T). It has also been funded by Project PR2024-028, Call for Grants to Research and Teaching Staff of the University of Cadiz, Plan Propio 2022-2023, and by Project FEDER-UCA-2024-A2-48, Regional Ministry of University, Research and Innovation of the Government of Andalusia, Programa Operativo FEDER Andalucía 2021-2027.
DS Repositorio Institucional de la Universidad de Cádiz
RD 08-nov-2026