Clément Quinton

Clément Quinton

DEEP-SE Politecnico

I am a postdoctoral fellow at Politecnico di Milano, where I work with Luciano Baresi in the DEEP-SE group.

I am interested in (dynamic) software product lines, variability modeling and software evolution. In particular, my work focuses on the definition, modeling, analysis and evolution management of highly configurable systems.


From October 2011 to October 2014, I was a PhD student in the SPIRALS team from the Inria Lille and the University of Lille 1. You can learn more about my PhD here.

From 2009 to 2011, I was software engineer in the ADAM team and I worked on the development of a framework to design and automatically configure applications for smartphones.

Back to Top


You can also consult the HAL server, the Google Scholar search engine or the DBLP index.

We know it well that none of us acting alone can achieve success.

— Nelson Mandela, Inaugural Address, May 10th 1994


SmartyCo: Managing Cyber-Physical Systems for Smart Environments. Daniel Romero, Clément Quinton, Laurence Duchien, Lionel Seinturier, and Carolina Valdez. Accepted in the European Conference on Software Architecture, ECSA'15. Dubrovnik, Croatia, 07-11 Septembre 2015.

Evolution in Dynamic Software Product Lines: Challenges and Perspectives. Clément Quinton, Rick Rabiser, Michael Vierhauser, Paul Grünbacher and Luciano Baresi. In Proceedings of the 19th International Software Product Line Conference, SPLC'15. Nashville, USA, 20-24 July 2015.

Dynamically Evolving the Structural Variability of Dynamic Software Product Lines. Luciano Baresi and Clément Quinton. In Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS'15. Florence, Italy, 18-19 May 2015.

SALOON: A Platform for Selecting and Configuring Cloud Environments. Clément Quinton, Daniel Romero and Laurence Duchien. In Software: Practice and Experience, 2015, to appear (available online).


Cloud Environment Selection and Configuration: A Software Product Lines-Based Approach. PhD thesis.

Consistency Checking for the Evolution of Cardinality-based Feature Models. Clément Quinton, Andreas Pleuss, Daniel Le Berre, Laurence Duchien and Goetz Botterweck. In Proceedings of the 18th International Software Product Line Conference, SPLC'14. Florence, Italy, 15-19 Septembre 2014.

Automated Selection and Configuration of Cloud Environments Using Software Product Lines Principles. Clément Quinton, Daniel Romero and Laurence Duchien. In Proceedings of the 7th IEEE International Conference on Cloud Computing, CLOUD'14. Anchorage, USA, 27-02 June/July 2014.


Cardinality-Based Feature Models With Constraints: A Pragmatic Approach. Clément Quinton, Daniel Romero and Laurence Duchien. In Proceedings of the 17th International Software Product Line Conference, SPLC'13. Tokyo, Japan, 26-30 August 2013.

SPLEMMA: A Generic Framework for Controlled Evolution of Software Product Lines. Daniel Romero, Simon Urli, Clément Quinton, Mireille Blay-Fornarino, Philippe Collet, Laurence Duchien and Sébastien Mosser. MAPLE/SCALE workshop, in Proceedings of the 17th International Software Product Line Conference, Volume 2, SPLC'13. Tokyo, Japan, 26 August 2013.

Towards Multi-Cloud Configurations Using Feature Models and Ontologies. Clément Quinton, Nicolas Haderer, Romain Rouvoy and Laurence Duchien. In Proceedings of the 1st International Workshop on Multi-Cloud Applications and Federated Clouds, Multi-Cloud'13. Prague, Czech Republic, 22 April 2013.


CAPucine: Context-Aware Service-Oriented Product Line for Mobile Apps. Carlos Parra, Clément Quinton and Laurence Duchien. ERCIM News, ERCIM EEIG, 2012, Special Theme Evolving Software, 88, pp. 38-39.

Feature Model Differences. Mathieu Acher, Patrick Heymans, Philippe Collet, Clément Quinton, Philippe Lahire and Philippe Merle. In Proceedings of the 24th International Conference on Advanced Information Systems Engineering, CAiSE'12. Gdansk, Poland, 25-29 June 2012.

Using Feature Modelling and Automations to Select among Cloud Solutions. Clément Quinton, Patrick Heymans and Laurence Duchien. In Proceedings of the 3rd International Workshop on Product Line Approaches in Software Engineering, PLEASE'12. Zurich, Switzerland, 2-9 June 2012.

Leveraging Feature Models to Configure Virtual Appliances. Clément Quinton, Romain Rouvoy and Laurence Duchien. In Proceedings of the 2nd International Workshop on Cloud Computing Platforms, CloudCP'12. Bern, Switzerland, 10-13 April 2012.


Using Multiple Feature Models to Design Applications for Mobile Phones. Clément Quinton, Sébastien Mosser, Carlos Parra and Laurence Duchien. MAPLE/SCALE workshop, in Proceedings of the 15th International Software Product Line Conference, Volume 2, SPLC'11. Munich, Germany, 22-26 August 2011.

National Workshops (french)

Vers un Outil de Configuration et de Déploiement pour les Nuages. Clément Quinton and Laurence Duchien. In 5ème Journée Lignes de Produits. Lille, France, 6 November 2012.

AppliDE: Modélisation et Génération d'Applications pour Smartphones. Clément Quinton, Christophe Demarey, Nicolas Dolet and Laurence Duchien. In 7ème Journées sur l'Ingénierie Dirigée par les Modèles. Lille, France, 7-10 June 2011.

Back to Top

PhD Thesis

I completed my PhD thesis, entitled Cloud Environment Selection and Configuration: A Software Product Lines-Based Approach, in October 2014. The PhD defense committee was as follows:

Best PhD Award   Best PhD Award from the French research group on Programming and Software Engineering. More information here (fr).


SALOON (from SoftwAre product Lines for clOud cOmputiNg), is the platform for selecting and configuring cloud environments I developed during my PhD. You can learn more about SALOON and watch demo videos here.


  In the recent years, cloud computing has become a major trend in distributed computing environments enabling software virtualization on configurable runtime environments. These environments provide numerous highly configurable software resources at different levels of functionality, that may lead to configuration errors when done manually. Therefore, cloud environments selection and configuration tools and approaches have been developed, ranging from ad-hoc implementation software, to automated strategies based on model transformation. However, these approaches suffer from a lack of abstraction, or do not provide an automated an scalable configuration reasoning support. Moreover, they are often limited to a certain type of cloud environment, thus limiting their efficiency.
  To address these shortcomings, and since we noticed that an important number of such cloud environments share several characteristics, we present in this thesis an approach based on software product line principles, with dedicated variability models to handle cloud environments commonalities and variabilities. Software product lines were defined to take advantage of commonalities through the definition of reusable artifacts, in order to automate the derivation of software products. In this dissertation, we provide in particular three major contributions. First, we propose an abstract model for feature modeling with attributes, cardinalities, and constraints over both of them. This kind of feature models are required to describe the variability of cloud environments. By providing an abstract model, we are thus implementation-independent and allow existing feature modeling approaches to rely on this model to extend their support. As a second contribution, we provide an automated support for maintaining the consistency of cardinality-based feature models. When evolving them, inconsistencies may arise due to the defined cardinalities and constraints over them, and detecting them can be tedious and complex whenever the size of the feature model grows. Finally, we provide as third contribution SALOON, a platform to select and configure cloud environments based on software product line principles. In particular, SALOON relies on our abstract model to describe cloud environments as feature models, and provide an automated support to derive configuration files and executable scripts, enabling the configuration of cloud environment in a reliable way.
  The experiments we conducted to validate our proposal show that by using software product lines and feature models, we are able to provide an automated, scalable, practical and reliable approach to select and configure cloud environments with respect to a set of requirements, even when numerous different kind of these environments are involved.

Back to Top