Variability modelling and test management for highly-configurable software

Open Postdoc position (2 years)

We are looking for a candidate holding a PhD in computer science, with a primary expertise in software engineering. The candidate should have an interest in software product lines, and ideally Software Testing. In addition to research activities, the candidate will be involved in project management tasks and direct interactions with industrial partners.

Contacts: Benoit Baudry (, Arnaud Gotlieb (, Mathieur Acher (

Keywords: Keywords: software product lines, variability management, software testing, highly-configurable software intensive system.


Scientific context

Software companies build more and more customizable software systems in order to expand their market through new functionalities and services, while still reusing and maintaining a common code base. In this project we are interesting in building explicit models of this variability in the system, in order to select, to prioritize and to generate test cases. The project will be in the domain of image recognition systems.
Image recognition systems rely on signals captured by video cameras, which are then processed through a chain of algorithms. Basic signal processing algorithms are assembled in different ways, depending on the goal of image recognition (scene interpretation, follow a specific object, etc.). Each algorithm is a complex piece of software that can be configured through multiple parameters. Additionally, these algorithms can be assembled in different chains corresponding to various recognition needs, and processing very different kind of images (day/night, lots/few contrast, etc.).
The main challenge for maintaining, evolving and testing these systems is to deal with the combinatorial explosion of (i) the number of configurations for individual components and of (ii) all different kinds of images the assemblies must handle. Several restrictions do exist when designing these configurations and images that can be captured in constraint models. However, there currently exists no systematic method to generate and qualify test configurations in this context.

Postdoc mission

The postdoc candidate will be in charge of setting a systematic technique and associated methodology to model the different variability dimensions and qualify test data sets for (i) basic signal processing algorithms and (ii) image recognition processing chains. The technique will leverage recent results for test selection in the context of software product lines [1,2,3] and variability model analysis [4].


[1] G. Perrouin, S. Sen, J. Klein, B. Baudry, and Y. Le Traon. Automated and scalable t-wise test case generation strategies for software product lines. In Proc. of ICST’10, pages 459-468, 2010.
[2] M. Fagereng Johansen, O. Haugen, and F. Fleurey. Properties of realistic feature models make combinatorial testing of product lines feasible. In Proc. of MODELS’11, pages 638-652, 2011.
[3] A. Hervieu, B. Baudry, and A. Gotlieb. Pacogen: Automatic generation of pairwise test configurations from feature models. In Proc. of ISSRE’11, pages 120-129, 2011.
[4] M. Acher, P. Collet, P. Lahire, and R. France. FAMILIAR: A Domain-Specific Language for Large Scale Management of Feature Models. In Science of Computer Programming (SCP), 2013.

Working Environment

The postdoc candidate will work at INRIA in the Triskell team. Triskell’s research is in the area of software engineering, focusing on model-driven engineering and software testing. The team is actively involved in European, French and industrial projects and is composed of 7 faculty members, 20 PhD students and 4 engineers. The position is already open and applications will be reviewed until the position is filled.
The monthly net salary is 2100 euros and the contract is for 24 months.