Hi there! Welcome to my personnal webpage where I will tell you about my work, publications, teaching activities and more !
Since September 2022, I am an associate professor at the University of Rennes. I joined the DiverSE team to tackle new challenges in software engineering, software testing, software variability, and machine learning!
From January 2019 to September 2022, I was a post-doc researcher at the Namur Digital Institute (NaDI) from the University of Namur (UNamur). I was involved in the EoS (Excellence of Science) VeriLearn project with Gilles Perrouin, Patrick Heymans, Benoit Frenay and Pierre-Yves Schobbens.
Previously, I obtained my PhD working at Université de Rennes and IRISA lab in the DiverSE working group in Rennes (France). My supervisors were Mathieu Acher and Jean-Marc JEZEQUEL. The thesis is entitled: Investigate the Matrix: Leveraging Variability to Specialize Software and Test Suites (more in the PhD topic section).
Furthermore, I am a tennis player. I began to play tennis at 6 until my 18. I stopped to focus on my studies and started again during my PhD.
After graduating in Computer Science with a fous on Data Image Processing (a 2-years diploma teachning fundamentals of a panel of facets of computer science, including OS, Programming, Mathematics, Web technologies, network managing, Systems design, etc.)from the Institute of Technology of Lannion (France); I went to ESIR (Ecole Supérieure d’Ingénieurs de Rennes, Rennes, France), an engeeniering school where I chose to continue focusing on Data Image Processing. During the last year of ESIR, I also graduated from the University of Rennes 1 with a Master in Computer Science with a focus on introducing the world of research to student. Thus, I have a strong background in Data Image Processing (as well as Machine Learning) on one hand and in Softwarer Engineering thanks to my PhD on the other hand.
As my experience in research grows and I explore new ideas, my topics of interest also grow larger and larger… Here is a list, but it is clearly opened to include more topics:
The EoS VeriLearn Project is a blue sky project trying to think about how Machine Learning and other Artificial Intelligence techniques can be tested and/or verified. Deep Learning is not considered for now in this context. The project gathers different university of Belgium including universities from Brussels, Leuven and Namur. Different aspects of testing, verifications as well as ML are tackled in this project. I am more particularly involved in the testing research direction, including performance aspects; but also, related to my PhD how to mitigate risks when using ML prediction models in the context of Software Product Lines. This work is mainly done in Namur with Patrick Heymans and Gilles Perrouin. I am also involved in the ethics work package in which we try to evaluate risks and consequences of blindly using systems based on ML in critical contexts such as justice or security. This work is done in collaboration with the University of Leuven (more particularly Pieter Delobelle and Bettina Berendt) and the University of Namur. Finally, I help also in trying to enforce domain-knowledge constraints directly in ML classifier implementations to make as efficient as possible but explainable and understandable for domain experts. This work is currently done with Geraldin Nanfack and Benoit Frenay from the University of Namur.
title: Investigate the Matrix: Leveraging Variability to Specialize Software and Test Suites manuscript and slides
My PhD tackles both the problem of testing configurable systems and improving the quality of test suites (in particular in the context of configurable systems). Modern software are configurable since they are designed to appeal to the largest possible number of users via customization and configuration. This makes software evermore complex, hard to design, code, test and maintain. In particular, how can we help users, having a specific application in mind or very specific requirements (for instance in terms of performance), in finding a proper configuration that is likely to meet their requirements? It is very challenging since the number of configuration options is so big (about 13k for the Linux Kernel) that it is usually impossible to generate all the possible variants in order to assess whether they comply with given requirements. In addition, configuration constraints (telling which combination of options are allowed or not) complexify the configuration space introducing even more computation problems.
The other part of my PhD is about how can we improve the quality of test suites designed to test configurable systems? Usually, several test cases are needed in order to properly test a piece of software. It is still the case with configurable software but the problem is that different configurations may behave differently depending on the given inputs, multiplying the number of tests needed to assess the quality of the system (globally). Because of the different behaviors of configurations, finding good (performance) test cases is difficult and I tried to tackle this challenge.
I have applied these different directions to different systems from different domains ranging from code compilers to computer vision based systems including machine learning based systems.
My internship took place in the TEXMEX (now LinkMedia )working group under the supervision of Ewa Kijak and Laurent Amsaleg. The topic of this internship was the security of Machine Learning processes applied to multimedia content which followed the work of Thanh-Toan Do and his PhD. The goal of this internship was to have a better understanding of how Machine Learning techniques can be influenced in the establishment of their separating functions depending on data that they have seen. An other concern was how sensitive the establishment of separating functions is w.r.t. the distribution of data points. This topic is very close to the work conducted by Battista Biggio and the PRALab in Cagliari.
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
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
I was PC member of the SPLC’19 Artifacts Track organized in Paris from the 9th to the 13th of September 2019. In 2020, I was PC member of the Artifact Track but also the Challenge Solution Track of SPLC’20 organized in Montreal, Canada but held online due to the Covid19 pandemic situation from the 19th to the 23th of October 2020. In 2022, I was PC member for the solution track of SPLC’22 held in Gratz and I served as a PC member for VaMoS. In 2023, I was again PC member for the solution track but also for the research track at SPLC’23 held in Tokyo. I served also as a PC member for ASE’23 held in Luxembourg.
In 2021, I served as a co-chair of the complete Challenge Track (evaluation of new cases but also solutions) at SPLC’21 organized in Leicester, England, UK from the 6th to the 11th of September 2021.
I have also served as a PC member for the FAT* 2020 conference (now named FAccT) and MaLTeSQuE 2021.
I made reviews for:
In 2019 and 2023, I received a Best Reviewer Award from SoSyM that recognizes me as one of the “Top 1% of SoSyM Reviewers”. I am now on the distinguished TOSEM reviewers board.
I am currently helping in the supervision of Antoine Gratia who made a Master’s thesis on modeling the variability of CNN architectures and that is now starting a PhD at the university of Namur under the supervision of Dr. Gilles Perrouin and Prof. Pierre-Yves Schobbens. During my post-doc, I also supervised two other PhD students: Géraldin Nanfack (PhD student under the supervision of Prof. Benoît Frénay) and Sophie Fortz (PhD student under the supervision of Dr. Gilles Perrouin).
During my PhD, I have worked with Clémentine Delambily, Hugo Martin and Léo Noël-Baron; all of them were brillant student helping during summers 2017 and 2018. In 2023, I supervised Camille Molinier who performed his internship at the University of Namur under the supervision of Dr. Gilles Perrouin.
In June 2019, I was member of the jury (president jury) at the defense of Samraa Alzubi. She defended her Master thesis in cybersecurity entitled Black-Box Adversarial Reprogramming Attack Against Convolutional Neural Networks Using Genetic Algorithm.
The same year, I was also involved in the jury evaluating the work of Simon Genin to obtain his Master’s degree. This Master’s thesis was supervised by Prof. Benoît Frénay and Prof. Benoît Vanderose.
Since then, I am involved in the jury of 2 masters students at the university of Namur every year. These juries involve: Céline Delhaye, Hugo Devillers, Piotr Banach, Audrey Gilson, Olivier Chevalier and Oliver Welcomme.
during the universitary year 2023-2024:
during the universitary year 2022-2023:
during the universitary year 2021-2022:
during the universitary year 2020-2021:
during the universitary year 2019-2020:
during the universitary year 2017-2018:
during the universitary year 2016-2017:
during the universitary year 2015-2016:
during the universitary year 2015-2014 (at ENSICaen):