Ph.D
Group : Verification of Algorithms, Languages and Systems

Tools and Techniques for the Verification of Modular Stateful Code

Starts on 01/05/2015
Advisor : FILLIÂTRE, Jean-Christophe

Funding : Bourse pour étudiant étranger
Affiliation : Université Paris-Saclay
Laboratory : LRI - VALS

Defended on 10/12/2018, committee :

Research activities :

Abstract :
This thesis is set in the field of formal methods, more precisely in the domain of deductive program verification. Our working context is the Why3 framework, a set of tools to implement, formally specify, and prove programs using off-the-shelf theorem provers. Why3 features a programming language, called WhyML, designed with verification in mind. An important feature of WhyML is ghost code: portions of the program that are introduced for the sole purpose of specification and verification. When it comes to get an executable implementation, ghost code is removed by an automatic process called extraction. One of the main contributions of this thesis is the formalization and implementation of Why3's extraction. The formalization consists in showing that the extracted program preserves the same operational behavior as the original source code, based on a type and effect system. The new extraction mechanism has been successfully used to get correct-by-construction OCaml modules, which are part of a verified OCaml library of data structures and algorithms. This verification effort led to two other contributions of this thesis. The first is a systematic approach to the verification of pointer-based data structures using ghost models of fragments of the heap. A fully automatic verification of a union-find data structure was achieved using this technique. The second contribution is a modular way to reason about iteration, independently of the underlying implementation. Several cursors and higher-order iterators have been specified and verified with this approach.

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.