Ph.D
Group : Formal Testing and System Exploration

Using Combinatorial Structures for Statistical Testing

Starts on 01/09/2000
Advisor : GAUDEL, Marie-Claude
[Alain Denise et Marie-Claude Gaudel]

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

Defended on 24/06/2004, committee :

Research activities :
   - Verification
   - Software Testing
   - Formal Methods for Software Engineering

Abstract :
In this thesis, we describe a new generic method for statistical testing
of software procedures, according to any given graphical description
of the behavior of the system under test (control flow graph,
statecharts, etc.). Its main originality is that it combines results
and tools from combinatorics (random generation of combinatorial
structures) with symbolic constraint solving, yielding a fully automatic
test generation method.
Instead of drawing input values as with classical testing methods,
uniform random generation routines are used for drawing paths from
the set of possible execution paths or traces of the system under test.
Then a constraint resolution step is used for finding actual values for
activating the generated paths.
Moreover, we show how linear programming techniques may help to improve the quality of test set.

A first application has been performed for structural statistical testing,
first defined by Thevenod-Fosse and Waeselynck (LAAS) and a tool has
been developed. Some experiments (more than 10000 on four programs
of an industrial software) has been made in order to evaluate our
approach and its stability.

These experiments show that our approach is comparable to the one of
the LAAS, is stable and has the additional advantage to be completely
automated. Moreover, these first experiments show also that the
method scales up well.
More generally, this approach could provide a basis for a new class of
tools in the domain of software testing, combining random generation of
combinatorial structures, linear programming techniques, and
constraint solvers.

More information: http://tel.archives-ouvertes.fr/docs/00/05/17/76/PDF/these_gouraud.pdf

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.