Infra-technology and economic performance: evidence from the United Kingdom measurement infrastructure. - Paul Temple; Geoffrey Williams (Inf. Econ. Policy, 2002)

Using machine learning to infer constraints for product lines. - Paul Temple; José Angel Galindo; Mathieu Acher; Jean-Marc Jézéquel (SPLC, 2016)

Learning Contextual-Variability Models. - Paul Temple; Mathieu Acher; Jean-Marc Jézéquel; Olivier Barais (IEEE Softw., 2017)

Multimorphic testing. - Paul Temple; Mathieu Acher; Jean-Marc Jézéquel (ICSE (Companion Volume), 2018)

Towards Estimating and Predicting User Perception on Software Product Variants. - Jabier Martinez; Jean-Sébastien Sottet; Alfonso García Frey; Tegawendé F. Bissyandé; Tewfik Ziadi; Jacques Klein; Paul Temple; Mathieu Acher; Yves Le Traon (ICSR, 2018)

VaryLATEX: Learning Paper Variants That Meet Constraints. - Mathieu Acher; Paul Temple; Jean-Marc Jézéquel; José Angel Galindo; Jabier Martinez; Tewfik Ziadi (VaMoS, 2018)

Towards Adversarial Configurations for Software Product Lines. - Paul Temple; Mathieu Acher; Battista Biggio; Jean-Marc Jézéquel; Fabio Roli (CoRR, 2018)

Investigate the matrix: leveraging variability to specialize software and test suites. (Examinons la matrice: s'appuyer sur la variabilité pour spécialiser les systèmes et les suites de tests). - Paul Temple (2018)

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

Towards quality assurance of software product lines with adversarial configurations. - Paul Temple; Mathieu Acher; Gilles Perrouin; Battista Biggio; Jean-Marc Jézéquel; Fabio Roli (SPLC (A), 2019)

Towards Learning-Aided Configuration in 3D Printing: Feasibility Study and Application to Defect Prediction. - Benoit Amand; Maxime Cordy; Patrick Heymans; Mathieu Acher; Paul Temple; Jean-Marc Jézéquel (VaMoS, 2019)

VaryMinions: leveraging RNNs to identify variants in event logs. - Sophie Fortz; Paul Temple; Xavier Devroey; Patrick Heymans; Gilles Perrouin (MaLTeSQuE@ESEC/SIGSOFT FSE, 2021)

Global explanations with decision rules: a co-learning approach. - Géraldin Nanfack; Paul Temple; Benoît Frénay (UAI, 2021)

Empirical assessment of generating adversarial configurations for software product lines. - Paul Temple; Gilles Perrouin; Mathieu Acher; Battista Biggio; Jean-Marc Jézéquel; Fabio Roli (Empir. Softw. Eng., 2021)

Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning. - Pieter Delobelle; Paul Temple; Gilles Perrouin; Benoît Frénay; Patrick Heymans; Bettina Berendt (SIGKDD Explor., 2021)

Empirical Assessment of Multimorphic Testing. - Paul Temple; Mathieu Acher; Jean-Marc Jézéquel (IEEE Trans. Software Eng., 2021)

Constraint Enforcement on Decision Trees: A Survey. - Géraldin Nanfack; Paul Temple; Benoît Frénay (ACM Comput. Surv., 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)

Explicit or Implicit? On Feature Engineering for ML-based Variability-intensive Systems. - Paul Temple; Gilles Perrouin (VaMoS, 2023)

Learning Customised Decision Trees for Domain-knowledge Constraints. - Géraldin Nanfack; Paul Temple; Benoît Frénay (Pattern Recognit., 2023)

FairPipes: Data Mutation Pipelines for Machine Learning Fairness. - Camille Molinier; Paul Temple; Gilles Perrouin (AST@ICSE, 2024)

CNNGen: A Generator and a Dataset for Energy-Aware Neural Architecture Search. - Antoine Gratia; Hong Liu 009; Shin'ichi Satoh 001; Paul Temple; Pierre-Yves Schobbens; Gilles Perrouin (ESANN, 2024)

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

Learning input-aware performance models of configurable systems: An empirical evaluation. - Luc Lesoil; Helge Spieker; Arnaud Gotlieb; Mathieu Acher; Paul Temple; Arnaud Blouin; Jean-Marc Jézéquel (J. Syst. Softw., 2024)

VaryMinions: leveraging RNNs to identify variants in variability-intensive systems' logs. - Sophie Fortz; Paul Temple; Xavier Devroey; Patrick Heymans; Gilles Perrouin (Empir. Softw. Eng., 2024)

Breaking the Loop: AWARE is the New MAPE-K. - Brell Peclard Sanwouo Chekam; Clément Quinton; Paul Temple (SIGSOFT FSE Companion, 2025)