Publications

• Brell Sanwouo, Clément Quinton, Paul Temple. Generative AI-based Adaptation in Microservices Architectures: A Systematic Mapping Study. ICWS’25

• Martin Molli, Daniel Balouek, Paul Temple, Thomas Ledoux. Facilitating Heterogeneity Management on the Computing Continuum. COMPAS’25

• Brell Peclard Sanwouo, Paul Temple, Clément Quinton. Breaking the Loop: AWARE is the new MAPE-K. FSE 2025

• Camille Molinier, Paul Temple, Zendra Olivier, Olivier Barais. Approches pour le renforcement d’une IA embarquée face aux attaques perturbant le federated learning. RESSI’25

• Brell Peclard Sanwouo, Clément Quinton, Paul Temple. Toward AI-based Complex Self-Adaptive Systems. BENEVOL’24

• Antoine Gratia, Hong Liu, Shin Ichi Satoh, Paul Temple, Pierre-Yves Schobbens, and Gilles Perrouin. Cnngen: A generator and a dataset for energy-aware neural architecture search. ESANN 2024

• Antoine Gratia, Paul Temple, Pierre-Yves Schobbens, and Gilles Perrouin. Energy-Aware Neural Architecture Search: Leveraging Genetic Algorithms for Balancing Performance and Consumption WIVACE Workshop 2024.

• Sophie Fortz, Paul Temple, Xavier Devroey, and Gilles Perrouin. Towards Feature-based ML-enabled Behaviour Location. VaMoS 2024

• Luc Lesoil, Helge Spieker, Arnaud Gotlieb, Mathieu Acher, Paul Temple, Arnaud Blouin, and Jean-Marc Jézéquel. Learning input-aware performance models of configurable systems: An empirical evaluation. JSS 2024

• Sophie Fortz, Paul Temple, Xavier Devroey, Patrick Heymans, and Gilles Perrouin. VaryMinions: Leveraging RNNs to Identify Variants in Variability-intensive Systems’ Logs. EMSE 2024

• Camille Molinier, Paul Temple, and Gilles Perrouin. FairPipes: Data Mutation Pipelines for Machine Learning Fairness. AST 2024

• Géraldin Nanfack, Paul Temple, and Benoit Frénay. Learning customised decision trees for domain-knowledge constraints. Pattern Recognition 2023

• Paul Temple and Gilles Perrouin. Explicit or Implicit? On Feature Engineering for ML-based Variability-intensive Systems. VaMoS 2023

• Paul Temple, Mathieu Acher, Jean-Marc Jézéquel. Empirical Assessment of Multimorphic Testing. IEEE Transactions on Software Engineering (TSE) 2019

• Paul Temple, Gilles Perrouin, Mathieu Acher, Battista Biggio, Jean-Marc Jézéquel, Fabio Roli. Empirical Assessment of Generating Adversarial Configurations for Software Product Lines. EMSE 2020 Special Issue

• Géraldin Nanfack, Paul Temple, and Benoit Frénay. Constraint Enforcement on Decision Trees: a Survey. ACM Computing Surveys 2022

• Pieter Delobelle, Paul Temple, Gilles Perrouin, Benoît Frénay, Patrick Heymans, and Bettina Berendt. Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning. ACM SIGKDD Explorations Newsletter, June 2021

• Géraldin Nanfack, Paul Temple, and Benoit Frénay. Global Explanations with Decision Rules: a Co-learning Approach. Conference on Uncertainty in Artificial Intelligence (UAI), July 2021, Online

• Sophie Fortz, Paul Temple, Xavier Devroey, Patrick Heymans, Gilles Perrouin. VaryMinions: leveraging RNNs to identify variants in event logs. 5th International Workshop on Machine Learning Techniques for Software Quality Evolution (MaLTeSQUE), August 21, Online

• Juliana Alves Pereira, Hugo Martin, Mathieu Acher, Paul Temple. Machine Learning and Configurable Systems: A Gentle Introduction. (Online) Software Product Line Conference (SPLC), Sep 2020, Montreal, Canada (Tutorial Session)

• Paul Temple, Mathieu Acher, Gilles Perrouin, Battista Biggio, Jean-Marc Jézéquel, Fabio Roli. Towards Quality Assurance of Software Product Lines with Adversarial Configurations. Software Product Line Conference (SPLC), Sep 2019, Paris, France

• Hugo Martin, Juliana Alves Pereira, Mathieu Acher, Paul Temple. Machine Learning and Configurable Systems: A Gentle Introduction. Software Product Line Conference (SPLC), Sep 2019, Paris, France (Tutorial Session)

• Paul Temple, Gilles Perrouin, Benoît Frénay, Pierre-Yves Schobbens. Customizing Adversarial Machine Learning to Test Deep Learning Techniques. 1st Workshop on Deep Learning<=> Testing (co-located with ICSE’19), May 2019, Montréal, Canada

• Paul Temple, Hugo MARTIN, Mathieu ACHER, Jean-Marc Jézéquel. Applying Multimorphic Testing to Deep Learning Systems. 1st Workshop on Deep Learning<=> Testing (co-located with ICSE’19), May 2019, Montréal, Canada

• Benoit Amand, Maxime Cordy, Patrick Heymans, Mathieu Acher, Paul Temple, Jean-Marc Jézéquel. Towards Learning-Aided Configuration in 3D Printing: Feasibility Study and Application to Defect Prediction. 13th International Workshop on Variability Modelling of Software-Intensive Systems (VaMoS), Feb 2019, Leuven, Belgium

• Paul Temple, Mathieu Acher, Jean-Marc Jézéquel. Multimorphic Testing. 40th International Conference on Software Engineering (ICSE), May 2018, Gothenburg, Sweden (Poster Session)

• Jabier Martinez, Jean-Sébasten Sottet, Alfonso Garcia Frey, Tegawendé Bissyandé, Tewfik Ziadi, Jacques Klein, Paul Temple, Mathieu Acher, Yves Le Traon. Towards Estimating and Predicting User Perception on Software Product Variants. International Conference on Software Reuse (ICSR), May 2018, Madrid, Spain

• Mathieu Acher, Paul Temple, Jean-Marc Jézéquel, José Angel Galindo Duarte, Jabier Martinez, Tewfik Ziadi. VaryLaTeX: Learning Paper Variants That Meet Constraints. 12th International Workshop on Variability Modelling of Software-Intensive Systems (VaMoS), Feb 2018, Madrid, Spain

• Paul Temple, Mathieu Acher, Jean-Marc Jézéquel, Olivier Barias. Learning Contextual-Variability Models. IEEE Software Special Issue on Context Aware and Smart Healthcare, Novembre-Décembre 2017

• Paul Temple, José Angel Galindo Duarte, Mathieu Acher, Jean-Marc Jézéquel. Using Machine Learning to Infer Constraints for Product Lines. Software Product Line Conference (SPLC), Sep 2016, Beijing, China

• Paul Temple, Mathieu Acher, Battista Biggio, Jean-Marc Jézéquel, Fabio Roli. Towards Adversarial Configurations for Software Product Lines

• Paul Temple, Mathieu Acher, Jean-Marc Jézéquel, Léo Noel-Baron, José Galindo. Learning-Based Performance Specialization of Configurable Systems