I am a 3-rd year PhD student working at Université de Rennes and IRISA lab in the DiverSE working group in Rennes (France).
I am working on testing Computer vision algorithms with the use of Software Engineering techniques. (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.
In the context of this PhD, we try to better understand how variability affects configurable systems in their performances. In particular, we focus on Computer Vision (CV) systems which are based on mathematical fundamentals. They are also configurable systems with options that can influence their performances. There are plenty of CV techniques developped to address different yet similar issues (such as tracking objects in videos or tracking pedestrians). Choosing a CV configuration that will give the best performances w.r.t. an input (i.e., videos or inputs) is a non-trivial process. Inputs can have features that affect drastically the performances (e.g., execution time, memory consumption or even capability of performing the task at hand) of the CV technique. Additionally, the configuration of the system can have an impact on its performances.
Part of my work is to leverage Machine Learning techniques in order to understand how the CV configuration, on one hand, and given inputs, on the other, affect the performance of the program. Part of my work focused on leveraging Machine Learning techniques to help users configuring their systems such that, by reducing the variability space (i.e., values that can be set to options), it will meet users’ performance requirements. An other part has been to evaluate the relative merits of a set of tests to distinguish different configurations of a system. In the end, assessing the quality of tests might help in detecting bugs, reducing the number of tests to execute, etc.
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.
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
I will be a PC member of the 1st Workshop on Computational Intelligence for Software Product Lines (CI4SPL) which will be held in conjunction with SPLC2018 in September 2018 in Gothenburg, Sweden.
during the universitary year 2017-2108:
during the universitary year 2016-2017:
during the universitary year 2015-2016:
during the universitary year 2015-2014 (at ENSICaen):