Ph.D
Group : Formal Testing and System Exploration

Approches combinatoires pour le test statistique à grande échelle

Starts on 01/10/2007
Advisor : GAUDEL, Marie-Claude
[co-encadrement avec Alain DENISE]

Funding : AM
Affiliation : Université Paris-Saclay
Laboratory : LRI

Defended on 19/11/2010, committee :

Research activities :
   - Software Testing
   - Model-Checking

Abstract :
This thesis focuses on the development of combinatorial methods for
testing and formal verification of software. We are interested in
solutions based on probabilistic approaches when exhaustivity is not
tractable.
For model-based testing, we guide the random exploration of the model
to ensure a satisfaction with desired probability of the expected
coverage criterion, regardless of the underlying topology of the
explored model.
Regarding model-checking, we show how to generate a random number of
finite paths to check if a property is satisfied with a certain
probability.
In the first part, we compare different algorithms for generating
uniformly at random paths in an automaton. Then we propose a new
algorithm that offers a good compromise with a sub-linear space
complexity in the path length and a almost-linear time complexity.
This algorithm allows the exploration of large models (tens of
millions of states) by generating long paths (hundreds of thousands of
transitions).
In a second part, we present a way to combine partial order reduction
and on-the- fly generation techniques to explore concurrent systems
without constructing the global model, but relying on models of the
components only.
Finally, we show how to bias the previous algorithms to satisfy
coverage criteria that are not based on paths, but on a set of states
or transitions. We propose a mixed solution to ensure both various
ways of exploring those states or transitions and the criterion
satisfaction with a desired probability.

CAUSAL LEARNING FOR DIAGNOSTIC SUPPORT


CAUSAL UNCERTAINTY QUANTIFICATION UNDER PARTIAL KNOWLEDGE AND LOW DATA REGIMES


MICRO VISUALIZATIONS: DESIGN AND ANALYSIS OF VISUALIZATIONS FOR SMALL DISPLAY SPACES
The topic of this habilitation is the study of very small data visualizations, micro visualizations, in display contexts that can only dedicate minimal rendering space for data representations. For several years, together with my collaborators, I have been studying human perception, interaction, and analysis with micro visualizations in multiple contexts. In this document I bring together three of my research streams related to micro visualizations: data glyphs, where my joint research focused on studying the perception of small-multiple micro visualizations, word-scale visualizations, where my joint research focused on small visualizations embedded in text-documents, and small mobile data visualizations for smartwatches or fitness trackers. I consider these types of small visualizations together under the umbrella term ``micro visualizations.'' Micro visualizations are useful in multiple visualization contexts and I have been working towards a better understanding of the complexities involved in designing and using micro visualizations. Here, I define the term micro visualization, summarize my own and other past research and design guidelines and outline several design spaces for different types of micro visualizations based on some of the work I was involved in since my PhD.