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Paul Temple
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2020 – today
- 2024
- [j9]Sophie Fortz
, Paul Temple
VaryMinions: leveraging RNNs to identify variants in variability-intensive systems' logs. Empir. Softw. Eng. 29(4): 99 (2024) - [j8]Luc Lesoil, Helge Spieker, Arnaud Gotlieb, Mathieu Acher, Paul Temple, Arnaud Blouin, Jean-Marc Jézéquel:
Learning input-aware performance models of configurable systems: An empirical evaluation. J. Syst. Softw. 208: 111883 (2024) - [c12]Sophie Fortz
Towards Feature-based ML-enabled Behaviour Location. VaMoS 2024: 152-154 - 2023
- [j7]Géraldin Nanfack
Learning Customised Decision Trees for Domain-knowledge Constraints. Pattern Recognit. 142: 109610 (2023) - [c11]Paul Temple
Explicit or Implicit? On Feature Engineering for ML-based Variability-intensive Systems. VaMoS 2023: 91-93 - [p1]Hugo Martin, Paul Temple, Mathieu Acher, Juliana Alves Pereira, Jean-Marc Jézéquel:
Machine Learning for Feature Constraints Discovery. Handbook of Re-Engineering Software Intensive Systems into Software Product Lines 2023: 175-196 - 2022
- [j6]Géraldin Nanfack
Constraint Enforcement on Decision Trees: A Survey. ACM Comput. Surv. 54(10s): 201:1-201:36 (2022) - 2021
- [j5]Paul Temple
Empirical assessment of generating adversarial configurations for software product lines. Empir. Softw. Eng. 26(1): 6 (2021) - [j4]Pieter Delobelle, Paul Temple
Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning. SIGKDD Explor. 23(1): 32-41 (2021) - [j3]Paul Temple
Empirical Assessment of Multimorphic Testing. IEEE Trans. Software Eng. 47(7): 1511-1527 (2021) - [c10]Sophie Fortz
VaryMinions: leveraging RNNs to identify variants in event logs. MaLTeSQuE@ESEC/SIGSOFT FSE 2021: 13-18 - [c9]Géraldin Nanfack, Paul Temple, Benoît Frénay:
Global explanations with decision rules: a co-learning approach. UAI 2021: 589-599 - 2020
- [c8]Juliana Alves Pereira, Hugo Martin, Paul Temple, Mathieu Acher:
Machine learning and configurable systems: a gentle introduction. SPLC (A) 2020: 40:1 - [i3]Pieter Delobelle, Paul Temple
Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning. CoRR abs/2005.06852 (2020)
2010 – 2019
- 2019
- [c7]Paul Temple
Towards quality assurance of software product lines with adversarial configurations. SPLC (A) 2019: 38:1-38:12 - [c6]Hugo Martin, Juliana Alves Pereira, Mathieu Acher
Machine learning and configurable systems: a gentle introduction. SPLC (A) 2019: 48:1-48:2 - [c5]Benoit Amand, Maxime Cordy, Patrick Heymans, Mathieu Acher, Paul Temple
Towards Learning-Aided Configuration in 3D Printing: Feasibility Study and Application to Defect Prediction. VaMoS 2019: 7:1-7:9 - [i2]Paul Temple, Mathieu Acher, Gilles Perrouin, Battista Biggio, Jean-Marc Jézéquel, Fabio Roli:
Towards Quality Assurance of Software Product Lines with Adversarial Configurations. CoRR abs/1909.07283 (2019) - 2018
- [b1]Paul Temple:
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). University of Rennes 1, France, 2018 - [c4]Paul Temple, Mathieu Acher
Multimorphic testing. ICSE (Companion Volume) 2018: 432-433 - [c3]Jabier Martinez
Towards Estimating and Predicting User Perception on Software Product Variants. ICSR 2018: 23-40 - [c2]Mathieu Acher, Paul Temple
VaryLATEX: Learning Paper Variants That Meet Constraints. VaMoS 2018: 83-88 - [i1]Paul Temple, Mathieu Acher, Battista Biggio, Jean-Marc Jézéquel, Fabio Roli:
Towards Adversarial Configurations for Software Product Lines. CoRR abs/1805.12021 (2018) - 2017
- [j2]Paul Temple
Learning Contextual-Variability Models. IEEE Softw. 34(6): 64-70 (2017) - 2016
- [c1]Paul Temple, José Angel Galindo
Using machine learning to infer constraints for product lines. SPLC 2016: 209-218
2000 – 2009
- 2002
- [j1]Paul Temple, Geoffrey Williams:
Infra-technology and economic performance: evidence from the United Kingdom measurement infrastructure. Inf. Econ. Policy 14(4): 435-452 (2002)
Coauthor Index
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