Machine learning and configurable systems: a gentle introduction. - Hugo Martin 003; Juliana Alves Pereira; Mathieu Acher; Paul Temple (SPLC (A), 2019)

Sampling Effect on Performance Prediction of Configurable Systems: A Case Study. - Juliana Alves Pereira; Mathieu Acher; Hugo Martin 003; Jean-Marc Jézéquel (ICPE, 2020)

A comparison of performance specialization learning for configurable systems. - Hugo Martin 003; Mathieu Acher; Juliana Alves Pereira; Jean-Marc Jézéquel (SPLC (A), 2021)

Learning software configuration spaces: A systematic literature review. - Juliana Alves Pereira; Mathieu Acher; Hugo Martin 003; Jean-Marc Jézéquel; Goetz Botterweck; Anthony Ventresque (J. Syst. Softw., 2021)

Machine Learning for Performance Modelling on Colossal Software Configuration Spaces. (Apprentissage machine pour la modélisation de performances sur des espaces de configuration logiciels colossaux). - Hugo Martin 003 (2021)

Feature subset selection for learning huge configuration spaces: the case of linux kernel size. - Mathieu Acher; Hugo Martin 003; Luc Lesoil; Arnaud Blouin; Jean-Marc Jézéquel; Djamel Eddine Khelladi; Olivier Barais; Juliana Alves Pereira (SPLC (A), 2022)

Transferring Performance between Distinct Configurable Systems : A Case Study. - Luc Lesoil; Hugo Martin 003; Mathieu Acher; Arnaud Blouin; Jean-Marc Jézéquel (VaMoS, 2022)

Transfer Learning Across Variants and Versions: The Case of Linux Kernel Size. - Hugo Martin 003; Mathieu Acher; Juliana Alves Pereira; Luc Lesoil; Jean-Marc Jézéquel; Djamel Eddine Khelladi (IEEE Trans. Software Eng., 2022)

Machine Learning for Feature Constraints Discovery. - Hugo Martin 003; Paul Temple; Mathieu Acher; Juliana Alves Pereira; Jean-Marc Jézéquel (Handbook of Re-Engineering Software Intensive Systems into Software Product Lines, 2023)